Amazing-Python-Scripts

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{
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  {
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   "cell_type": "code",
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   "execution_count": 3,
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   "metadata": {
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    "id": "3kNEtx5DX3SB"
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   },
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   "outputs": [],
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   "source": [
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    "import numpy as np\n",
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    "import pandas as pd\n",
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    "\n",
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    "import matplotlib.pyplot as plt\n",
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    "%matplotlib inline"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 4,
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   "metadata": {
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    "id": "oOmCg2yRX3SC"
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   },
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   "outputs": [],
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   "source": [
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    "df=pd.read_csv(r'/content/perrin-freres-monthly-champagne-.csv')"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 5,
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   "metadata": {
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    "colab": {
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     "base_uri": "https://localhost:8080/",
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     "height": 206
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    },
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    "id": "EKilUFB2X3SC",
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    "outputId": "2c297f33-b847-49f8-f14c-682198a4a30f"
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   },
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   "outputs": [
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    {
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     "data": {
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       "\n",
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       "  <div id=\"df-a9b68a61-8bfe-4a35-93c0-f84d5cd05ba3\">\n",
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       "    <div class=\"colab-df-container\">\n",
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       "      <div>\n",
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       "<style scoped>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
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       "    .dataframe tbody tr th {\n",
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       "        vertical-align: top;\n",
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       "    }\n",
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       "\n",
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       "    .dataframe thead th {\n",
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       "        text-align: right;\n",
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       "    }\n",
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       "</style>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "  <thead>\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th></th>\n",
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       "      <th>Month</th>\n",
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       "      <th>Perrin Freres monthly champagne sales millions ?64-?72</th>\n",
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       "    </tr>\n",
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       "  </thead>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
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       "      <th>0</th>\n",
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       "      <td>1964-01</td>\n",
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       "      <td>2815.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>1</th>\n",
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       "      <td>1964-02</td>\n",
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       "      <td>2672.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>2</th>\n",
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       "      <td>1964-03</td>\n",
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       "      <td>2755.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>3</th>\n",
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       "      <td>1964-04</td>\n",
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       "      <td>2721.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>4</th>\n",
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       "      <td>1964-05</td>\n",
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       "      <td>2946.0</td>\n",
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       "    </tr>\n",
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       "  </tbody>\n",
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       "</table>\n",
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       "</div>\n",
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       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9b68a61-8bfe-4a35-93c0-f84d5cd05ba3')\"\n",
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       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "              style=\"display:none;\">\n",
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       "        \n",
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       "       width=\"24px\">\n",
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       "    <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",
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       "  </svg>\n",
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       "      </button>\n",
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       "      \n",
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       "  <style>\n",
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       "    .colab-df-container {\n",
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       "      display:flex;\n",
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       "      flex-wrap:wrap;\n",
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       "      gap: 12px;\n",
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       "    }\n",
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       "\n",
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       "    .colab-df-convert {\n",
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       "      background-color: #E8F0FE;\n",
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       "      border: none;\n",
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       "      border-radius: 50%;\n",
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       "      cursor: pointer;\n",
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       "      display: none;\n",
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       "      fill: #1967D2;\n",
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       "      height: 32px;\n",
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       "      padding: 0 0 0 0;\n",
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       "      width: 32px;\n",
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       "    }\n",
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       "\n",
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       "    .colab-df-convert:hover {\n",
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       "      background-color: #E2EBFA;\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "      fill: #174EA6;\n",
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       "    }\n",
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       "\n",
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       "    [theme=dark] .colab-df-convert {\n",
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       "      background-color: #3B4455;\n",
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       "      fill: #D2E3FC;\n",
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       "    }\n",
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       "\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
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       "      background-color: #434B5C;\n",
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       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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       "      fill: #FFFFFF;\n",
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       "    }\n",
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       "  </style>\n",
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       "\n",
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       "      <script>\n",
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       "        const buttonEl =\n",
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       "          document.querySelector('#df-a9b68a61-8bfe-4a35-93c0-f84d5cd05ba3 button.colab-df-convert');\n",
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       "        buttonEl.style.display =\n",
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       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "\n",
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       "        async function convertToInteractive(key) {\n",
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       "          const element = document.querySelector('#df-a9b68a61-8bfe-4a35-93c0-f84d5cd05ba3');\n",
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       "          const dataTable =\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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       "                                                     [key], {});\n",
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       "          if (!dataTable) return;\n",
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       "\n",
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       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "            + ' to learn more about interactive tables.';\n",
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       "          element.innerHTML = '';\n",
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       "          dataTable['output_type'] = 'display_data';\n",
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       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          const docLink = document.createElement('div');\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
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       "          element.appendChild(docLink);\n",
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       "        }\n",
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       "      </script>\n",
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       "    </div>\n",
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       "  </div>\n",
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       "  "
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      ],
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      "text/plain": [
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       "     Month  Perrin Freres monthly champagne sales millions ?64-?72\n",
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       "0  1964-01                                             2815.0     \n",
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       "1  1964-02                                             2672.0     \n",
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       "2  1964-03                                             2755.0     \n",
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       "3  1964-04                                             2721.0     \n",
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       "4  1964-05                                             2946.0     "
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      ]
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     },
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     "execution_count": 5,
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     "metadata": {},
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     "output_type": "execute_result"
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    }
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   ],
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   "source": [
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    "df.head()"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 6,
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   "metadata": {
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    "colab": {
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     "base_uri": "https://localhost:8080/",
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     "height": 206
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    },
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    "id": "ny-QErC4X3SD",
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    "outputId": "9b314fa9-c690-47c8-8940-f04c2ad7d944"
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   },
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   "outputs": [
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    {
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     "data": {
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      "text/html": [
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       "\n",
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       "  <div id=\"df-b9858575-f663-42fa-b437-442b83fb14e8\">\n",
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       "  <thead>\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th></th>\n",
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       "      <th>Month</th>\n",
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       "      <th>Perrin Freres monthly champagne sales millions ?64-?72</th>\n",
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       "    </tr>\n",
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       "  </thead>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
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       "      <th>102</th>\n",
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       "      <td>1972-07</td>\n",
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       "      <td>4298.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>103</th>\n",
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       "      <td>1972-08</td>\n",
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       "      <td>1413.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>104</th>\n",
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       "      <td>1972-09</td>\n",
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       "      <td>5877.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>105</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>106</th>\n",
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       "      <td>Perrin Freres monthly champagne sales millions...</td>\n",
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       "      <td>NaN</td>\n",
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       "    </tr>\n",
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       "  </tbody>\n",
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       "</table>\n",
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       "</div>\n",
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       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b9858575-f663-42fa-b437-442b83fb14e8')\"\n",
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       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "              style=\"display:none;\">\n",
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       "        \n",
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       "  </svg>\n",
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       "      \n",
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       "    .colab-df-container {\n",
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       "      display:flex;\n",
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       "      flex-wrap:wrap;\n",
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       "      gap: 12px;\n",
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       "    }\n",
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       "\n",
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       "    .colab-df-convert {\n",
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       "      background-color: #E8F0FE;\n",
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       "      border: none;\n",
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       "      border-radius: 50%;\n",
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       "      cursor: pointer;\n",
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       "      display: none;\n",
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       "      fill: #1967D2;\n",
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       "      height: 32px;\n",
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       "      padding: 0 0 0 0;\n",
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       "      width: 32px;\n",
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       "    }\n",
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       "\n",
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       "    .colab-df-convert:hover {\n",
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       "      background-color: #E2EBFA;\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "      fill: #174EA6;\n",
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       "    }\n",
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       "\n",
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       "    [theme=dark] .colab-df-convert {\n",
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       "      background-color: #3B4455;\n",
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       "      fill: #D2E3FC;\n",
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       "    }\n",
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       "\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
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       "      background-color: #434B5C;\n",
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       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "      fill: #FFFFFF;\n",
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       "    }\n",
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       "  </style>\n",
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       "\n",
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       "      <script>\n",
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       "        const buttonEl =\n",
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       "          document.querySelector('#df-b9858575-f663-42fa-b437-442b83fb14e8 button.colab-df-convert');\n",
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       "        buttonEl.style.display =\n",
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       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "\n",
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       "        async function convertToInteractive(key) {\n",
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       "          const element = document.querySelector('#df-b9858575-f663-42fa-b437-442b83fb14e8');\n",
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       "          const dataTable =\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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       "                                                     [key], {});\n",
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       "          if (!dataTable) return;\n",
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       "\n",
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       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
325
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
326
       "            + ' to learn more about interactive tables.';\n",
327
       "          element.innerHTML = '';\n",
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       "          dataTable['output_type'] = 'display_data';\n",
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       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          const docLink = document.createElement('div');\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
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       "          element.appendChild(docLink);\n",
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       "        }\n",
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       "      </script>\n",
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       "    </div>\n",
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       "  </div>\n",
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       "  "
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      ],
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      "text/plain": [
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       "                                                 Month  \\\n",
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       "102                                            1972-07   \n",
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       "103                                            1972-08   \n",
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       "104                                            1972-09   \n",
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       "105                                                NaN   \n",
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       "106  Perrin Freres monthly champagne sales millions...   \n",
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       "\n",
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       "     Perrin Freres monthly champagne sales millions ?64-?72  \n",
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       "102                                             4298.0       \n",
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       "103                                             1413.0       \n",
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       "104                                             5877.0       \n",
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       "105                                                NaN       \n",
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       "106                                                NaN       "
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      ]
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     },
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     "execution_count": 6,
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     "metadata": {},
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     "output_type": "execute_result"
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    }
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   ],
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   "source": [
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    "df.tail()"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 7,
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   "metadata": {
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    "colab": {
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     "base_uri": "https://localhost:8080/"
370
    },
371
    "id": "cdqQ_miqX3SD",
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    "outputId": "12875c95-59e0-4d33-da77-b4bb58220e1e"
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   },
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   "outputs": [
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    {
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     "data": {
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      "text/plain": [
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       "(107, 2)"
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      ]
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     },
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     "execution_count": 7,
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     "metadata": {},
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     "output_type": "execute_result"
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    }
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   ],
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   "source": [
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    "df.shape"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 8,
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   "metadata": {
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    "colab": {
395
     "base_uri": "https://localhost:8080/",
396
     "height": 206
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    },
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    "id": "Im_PVQkzX3SD",
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    "outputId": "4931fcce-b942-4fd8-8ecf-b9c9f4d5033d"
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   },
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   "outputs": [
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    {
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     "data": {
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      "text/html": [
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       "\n",
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       "  <div id=\"df-7c4ca444-ad2c-4db4-ac26-1a00a1872c6c\">\n",
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       "  <thead>\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th></th>\n",
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       "      <th>Month</th>\n",
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       "      <th>Sales</th>\n",
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       "    </tr>\n",
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       "  </thead>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
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       "      <th>0</th>\n",
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       "      <td>1964-01</td>\n",
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       "      <td>2815.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>1</th>\n",
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       "      <td>1964-02</td>\n",
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       "      <td>2672.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>2</th>\n",
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       "      <td>1964-03</td>\n",
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       "      <td>2755.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>3</th>\n",
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       "      <td>1964-04</td>\n",
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       "      <td>2721.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>4</th>\n",
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       "      <td>1964-05</td>\n",
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       "      <td>2946.0</td>\n",
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       "    </tr>\n",
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       "  </tbody>\n",
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       "</table>\n",
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       "</div>\n",
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       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c4ca444-ad2c-4db4-ac26-1a00a1872c6c')\"\n",
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       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "              style=\"display:none;\">\n",
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       "        \n",
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       "       width=\"24px\">\n",
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       "    <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",
467
       "  </svg>\n",
468
       "      </button>\n",
469
       "      \n",
470
       "  <style>\n",
471
       "    .colab-df-container {\n",
472
       "      display:flex;\n",
473
       "      flex-wrap:wrap;\n",
474
       "      gap: 12px;\n",
475
       "    }\n",
476
       "\n",
477
       "    .colab-df-convert {\n",
478
       "      background-color: #E8F0FE;\n",
479
       "      border: none;\n",
480
       "      border-radius: 50%;\n",
481
       "      cursor: pointer;\n",
482
       "      display: none;\n",
483
       "      fill: #1967D2;\n",
484
       "      height: 32px;\n",
485
       "      padding: 0 0 0 0;\n",
486
       "      width: 32px;\n",
487
       "    }\n",
488
       "\n",
489
       "    .colab-df-convert:hover {\n",
490
       "      background-color: #E2EBFA;\n",
491
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
492
       "      fill: #174EA6;\n",
493
       "    }\n",
494
       "\n",
495
       "    [theme=dark] .colab-df-convert {\n",
496
       "      background-color: #3B4455;\n",
497
       "      fill: #D2E3FC;\n",
498
       "    }\n",
499
       "\n",
500
       "    [theme=dark] .colab-df-convert:hover {\n",
501
       "      background-color: #434B5C;\n",
502
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
503
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
504
       "      fill: #FFFFFF;\n",
505
       "    }\n",
506
       "  </style>\n",
507
       "\n",
508
       "      <script>\n",
509
       "        const buttonEl =\n",
510
       "          document.querySelector('#df-7c4ca444-ad2c-4db4-ac26-1a00a1872c6c button.colab-df-convert');\n",
511
       "        buttonEl.style.display =\n",
512
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
513
       "\n",
514
       "        async function convertToInteractive(key) {\n",
515
       "          const element = document.querySelector('#df-7c4ca444-ad2c-4db4-ac26-1a00a1872c6c');\n",
516
       "          const dataTable =\n",
517
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
518
       "                                                     [key], {});\n",
519
       "          if (!dataTable) return;\n",
520
       "\n",
521
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
522
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
523
       "            + ' to learn more about interactive tables.';\n",
524
       "          element.innerHTML = '';\n",
525
       "          dataTable['output_type'] = 'display_data';\n",
526
       "          await google.colab.output.renderOutput(dataTable, element);\n",
527
       "          const docLink = document.createElement('div');\n",
528
       "          docLink.innerHTML = docLinkHtml;\n",
529
       "          element.appendChild(docLink);\n",
530
       "        }\n",
531
       "      </script>\n",
532
       "    </div>\n",
533
       "  </div>\n",
534
       "  "
535
      ],
536
      "text/plain": [
537
       "     Month   Sales\n",
538
       "0  1964-01  2815.0\n",
539
       "1  1964-02  2672.0\n",
540
       "2  1964-03  2755.0\n",
541
       "3  1964-04  2721.0\n",
542
       "4  1964-05  2946.0"
543
      ]
544
     },
545
     "execution_count": 8,
546
     "metadata": {},
547
     "output_type": "execute_result"
548
    }
549
   ],
550
   "source": [
551
    "## Cleaning up the data\n",
552
    "df.columns=[\"Month\",\"Sales\"]\n",
553
    "df.head()"
554
   ]
555
  },
556
  {
557
   "cell_type": "code",
558
   "execution_count": 9,
559
   "metadata": {
560
    "id": "ftY8WNp5X3SE"
561
   },
562
   "outputs": [],
563
   "source": [
564
    "## Drop last 2 rows\n",
565
    "df.drop(106,axis=0,inplace=True)"
566
   ]
567
  },
568
  {
569
   "cell_type": "code",
570
   "execution_count": 10,
571
   "metadata": {
572
    "colab": {
573
     "base_uri": "https://localhost:8080/",
574
     "height": 206
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    },
576
    "id": "BatxXktGX3SE",
577
    "outputId": "f06b223a-5e33-40c7-c1d1-283cce2a1e41"
578
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579
   "outputs": [
580
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581
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       "      <th>101</th>\n",
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       "    <tr>\n",
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       "    .colab-df-convert {\n",
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       "                                                     [key], {});\n",
697
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698
       "\n",
699
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
700
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
701
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702
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712
       "  "
713
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714
      "text/plain": [
715
       "       Month   Sales\n",
716
       "101  1972-06  5312.0\n",
717
       "102  1972-07  4298.0\n",
718
       "103  1972-08  1413.0\n",
719
       "104  1972-09  5877.0\n",
720
       "105      NaN     NaN"
721
      ]
722
     },
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     "execution_count": 10,
724
     "metadata": {},
725
     "output_type": "execute_result"
726
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729
    "df.tail()"
730
   ]
731
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732
  {
733
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734
   "execution_count": 11,
735
   "metadata": {
736
    "id": "-wiS4LzzX3SE"
737
   },
738
   "outputs": [],
739
   "source": [
740
    "df.drop(105,axis=0,inplace=True)"
741
   ]
742
  },
743
  {
744
   "cell_type": "code",
745
   "execution_count": 12,
746
   "metadata": {
747
    "colab": {
748
     "base_uri": "https://localhost:8080/",
749
     "height": 206
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    },
751
    "id": "-dxqsIdyX3SE",
752
    "outputId": "292aab54-e91e-496c-ee62-d44048338528"
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   },
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   "outputs": [
755
    {
756
     "data": {
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       "  <div id=\"df-63dcaff1-75da-4f3e-ab21-1f2b2cfb974e\">\n",
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871
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       "\n",
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875
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876
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       "       Month   Sales\n",
891
       "100  1972-05  4618.0\n",
892
       "101  1972-06  5312.0\n",
893
       "102  1972-07  4298.0\n",
894
       "103  1972-08  1413.0\n",
895
       "104  1972-09  5877.0"
896
      ]
897
     },
898
     "execution_count": 12,
899
     "metadata": {},
900
     "output_type": "execute_result"
901
    }
902
   ],
903
   "source": [
904
    "df.tail()"
905
   ]
906
  },
907
  {
908
   "cell_type": "code",
909
   "execution_count": 13,
910
   "metadata": {
911
    "id": "oqXIa8mZX3SF"
912
   },
913
   "outputs": [],
914
   "source": [
915
    "# Convert Month into Datetime\n",
916
    "df['Month']=pd.to_datetime(df['Month'])"
917
   ]
918
  },
919
  {
920
   "cell_type": "code",
921
   "execution_count": 14,
922
   "metadata": {
923
    "colab": {
924
     "base_uri": "https://localhost:8080/",
925
     "height": 206
926
    },
927
    "id": "BQazqEoiX3SF",
928
    "outputId": "f54ccde4-a843-403a-e5bb-0da6d48eeb17"
929
   },
930
   "outputs": [
931
    {
932
     "data": {
933
      "text/html": [
934
       "\n",
935
       "  <div id=\"df-5a39ad6b-14e6-49e8-8c39-e6584a547518\">\n",
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       "    <div class=\"colab-df-container\">\n",
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       "      <div>\n",
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       "<style scoped>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "\n",
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       "    .dataframe tbody tr th {\n",
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       "        vertical-align: top;\n",
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       "    }\n",
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       "\n",
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       "    .dataframe thead th {\n",
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       "        text-align: right;\n",
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       "    }\n",
950
       "</style>\n",
951
       "<table border=\"1\" class=\"dataframe\">\n",
952
       "  <thead>\n",
953
       "    <tr style=\"text-align: right;\">\n",
954
       "      <th></th>\n",
955
       "      <th>Month</th>\n",
956
       "      <th>Sales</th>\n",
957
       "    </tr>\n",
958
       "  </thead>\n",
959
       "  <tbody>\n",
960
       "    <tr>\n",
961
       "      <th>0</th>\n",
962
       "      <td>1964-01-01</td>\n",
963
       "      <td>2815.0</td>\n",
964
       "    </tr>\n",
965
       "    <tr>\n",
966
       "      <th>1</th>\n",
967
       "      <td>1964-02-01</td>\n",
968
       "      <td>2672.0</td>\n",
969
       "    </tr>\n",
970
       "    <tr>\n",
971
       "      <th>2</th>\n",
972
       "      <td>1964-03-01</td>\n",
973
       "      <td>2755.0</td>\n",
974
       "    </tr>\n",
975
       "    <tr>\n",
976
       "      <th>3</th>\n",
977
       "      <td>1964-04-01</td>\n",
978
       "      <td>2721.0</td>\n",
979
       "    </tr>\n",
980
       "    <tr>\n",
981
       "      <th>4</th>\n",
982
       "      <td>1964-05-01</td>\n",
983
       "      <td>2946.0</td>\n",
984
       "    </tr>\n",
985
       "  </tbody>\n",
986
       "</table>\n",
987
       "</div>\n",
988
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a39ad6b-14e6-49e8-8c39-e6584a547518')\"\n",
989
       "              title=\"Convert this dataframe to an interactive table.\"\n",
990
       "              style=\"display:none;\">\n",
991
       "        \n",
992
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
993
       "       width=\"24px\">\n",
994
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
995
       "    <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",
996
       "  </svg>\n",
997
       "      </button>\n",
998
       "      \n",
999
       "  <style>\n",
1000
       "    .colab-df-container {\n",
1001
       "      display:flex;\n",
1002
       "      flex-wrap:wrap;\n",
1003
       "      gap: 12px;\n",
1004
       "    }\n",
1005
       "\n",
1006
       "    .colab-df-convert {\n",
1007
       "      background-color: #E8F0FE;\n",
1008
       "      border: none;\n",
1009
       "      border-radius: 50%;\n",
1010
       "      cursor: pointer;\n",
1011
       "      display: none;\n",
1012
       "      fill: #1967D2;\n",
1013
       "      height: 32px;\n",
1014
       "      padding: 0 0 0 0;\n",
1015
       "      width: 32px;\n",
1016
       "    }\n",
1017
       "\n",
1018
       "    .colab-df-convert:hover {\n",
1019
       "      background-color: #E2EBFA;\n",
1020
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1021
       "      fill: #174EA6;\n",
1022
       "    }\n",
1023
       "\n",
1024
       "    [theme=dark] .colab-df-convert {\n",
1025
       "      background-color: #3B4455;\n",
1026
       "      fill: #D2E3FC;\n",
1027
       "    }\n",
1028
       "\n",
1029
       "    [theme=dark] .colab-df-convert:hover {\n",
1030
       "      background-color: #434B5C;\n",
1031
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1032
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1033
       "      fill: #FFFFFF;\n",
1034
       "    }\n",
1035
       "  </style>\n",
1036
       "\n",
1037
       "      <script>\n",
1038
       "        const buttonEl =\n",
1039
       "          document.querySelector('#df-5a39ad6b-14e6-49e8-8c39-e6584a547518 button.colab-df-convert');\n",
1040
       "        buttonEl.style.display =\n",
1041
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1042
       "\n",
1043
       "        async function convertToInteractive(key) {\n",
1044
       "          const element = document.querySelector('#df-5a39ad6b-14e6-49e8-8c39-e6584a547518');\n",
1045
       "          const dataTable =\n",
1046
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1047
       "                                                     [key], {});\n",
1048
       "          if (!dataTable) return;\n",
1049
       "\n",
1050
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
1051
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1052
       "            + ' to learn more about interactive tables.';\n",
1053
       "          element.innerHTML = '';\n",
1054
       "          dataTable['output_type'] = 'display_data';\n",
1055
       "          await google.colab.output.renderOutput(dataTable, element);\n",
1056
       "          const docLink = document.createElement('div');\n",
1057
       "          docLink.innerHTML = docLinkHtml;\n",
1058
       "          element.appendChild(docLink);\n",
1059
       "        }\n",
1060
       "      </script>\n",
1061
       "    </div>\n",
1062
       "  </div>\n",
1063
       "  "
1064
      ],
1065
      "text/plain": [
1066
       "       Month   Sales\n",
1067
       "0 1964-01-01  2815.0\n",
1068
       "1 1964-02-01  2672.0\n",
1069
       "2 1964-03-01  2755.0\n",
1070
       "3 1964-04-01  2721.0\n",
1071
       "4 1964-05-01  2946.0"
1072
      ]
1073
     },
1074
     "execution_count": 14,
1075
     "metadata": {},
1076
     "output_type": "execute_result"
1077
    }
1078
   ],
1079
   "source": [
1080
    "df.head()"
1081
   ]
1082
  },
1083
  {
1084
   "cell_type": "code",
1085
   "execution_count": 15,
1086
   "metadata": {
1087
    "id": "AxU_xiv4X3SF"
1088
   },
1089
   "outputs": [],
1090
   "source": [
1091
    "df.set_index('Month',inplace=True)"
1092
   ]
1093
  },
1094
  {
1095
   "cell_type": "code",
1096
   "execution_count": 16,
1097
   "metadata": {
1098
    "colab": {
1099
     "base_uri": "https://localhost:8080/",
1100
     "height": 238
1101
    },
1102
    "id": "VOJRJJ0qX3SF",
1103
    "outputId": "6c090bb6-6e1c-4381-8df6-7a96d786b3ee"
1104
   },
1105
   "outputs": [
1106
    {
1107
     "data": {
1108
      "text/html": [
1109
       "\n",
1110
       "  <div id=\"df-8972e30d-e80c-4cad-b2e1-9fdb5a5293a5\">\n",
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       "    <div class=\"colab-df-container\">\n",
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       "      <div>\n",
1113
       "<style scoped>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
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       "\n",
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       "    .dataframe tbody tr th {\n",
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       "        vertical-align: top;\n",
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       "    }\n",
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       "\n",
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       "    .dataframe thead th {\n",
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       "        text-align: right;\n",
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       "    }\n",
1125
       "</style>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
1127
       "  <thead>\n",
1128
       "    <tr style=\"text-align: right;\">\n",
1129
       "      <th></th>\n",
1130
       "      <th>Sales</th>\n",
1131
       "    </tr>\n",
1132
       "    <tr>\n",
1133
       "      <th>Month</th>\n",
1134
       "      <th></th>\n",
1135
       "    </tr>\n",
1136
       "  </thead>\n",
1137
       "  <tbody>\n",
1138
       "    <tr>\n",
1139
       "      <th>1964-01-01</th>\n",
1140
       "      <td>2815.0</td>\n",
1141
       "    </tr>\n",
1142
       "    <tr>\n",
1143
       "      <th>1964-02-01</th>\n",
1144
       "      <td>2672.0</td>\n",
1145
       "    </tr>\n",
1146
       "    <tr>\n",
1147
       "      <th>1964-03-01</th>\n",
1148
       "      <td>2755.0</td>\n",
1149
       "    </tr>\n",
1150
       "    <tr>\n",
1151
       "      <th>1964-04-01</th>\n",
1152
       "      <td>2721.0</td>\n",
1153
       "    </tr>\n",
1154
       "    <tr>\n",
1155
       "      <th>1964-05-01</th>\n",
1156
       "      <td>2946.0</td>\n",
1157
       "    </tr>\n",
1158
       "  </tbody>\n",
1159
       "</table>\n",
1160
       "</div>\n",
1161
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8972e30d-e80c-4cad-b2e1-9fdb5a5293a5')\"\n",
1162
       "              title=\"Convert this dataframe to an interactive table.\"\n",
1163
       "              style=\"display:none;\">\n",
1164
       "        \n",
1165
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1166
       "       width=\"24px\">\n",
1167
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
1168
       "    <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",
1169
       "  </svg>\n",
1170
       "      </button>\n",
1171
       "      \n",
1172
       "  <style>\n",
1173
       "    .colab-df-container {\n",
1174
       "      display:flex;\n",
1175
       "      flex-wrap:wrap;\n",
1176
       "      gap: 12px;\n",
1177
       "    }\n",
1178
       "\n",
1179
       "    .colab-df-convert {\n",
1180
       "      background-color: #E8F0FE;\n",
1181
       "      border: none;\n",
1182
       "      border-radius: 50%;\n",
1183
       "      cursor: pointer;\n",
1184
       "      display: none;\n",
1185
       "      fill: #1967D2;\n",
1186
       "      height: 32px;\n",
1187
       "      padding: 0 0 0 0;\n",
1188
       "      width: 32px;\n",
1189
       "    }\n",
1190
       "\n",
1191
       "    .colab-df-convert:hover {\n",
1192
       "      background-color: #E2EBFA;\n",
1193
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1194
       "      fill: #174EA6;\n",
1195
       "    }\n",
1196
       "\n",
1197
       "    [theme=dark] .colab-df-convert {\n",
1198
       "      background-color: #3B4455;\n",
1199
       "      fill: #D2E3FC;\n",
1200
       "    }\n",
1201
       "\n",
1202
       "    [theme=dark] .colab-df-convert:hover {\n",
1203
       "      background-color: #434B5C;\n",
1204
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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       "      fill: #FFFFFF;\n",
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       "    }\n",
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       "  </style>\n",
1209
       "\n",
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       "      <script>\n",
1211
       "        const buttonEl =\n",
1212
       "          document.querySelector('#df-8972e30d-e80c-4cad-b2e1-9fdb5a5293a5 button.colab-df-convert');\n",
1213
       "        buttonEl.style.display =\n",
1214
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1215
       "\n",
1216
       "        async function convertToInteractive(key) {\n",
1217
       "          const element = document.querySelector('#df-8972e30d-e80c-4cad-b2e1-9fdb5a5293a5');\n",
1218
       "          const dataTable =\n",
1219
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1220
       "                                                     [key], {});\n",
1221
       "          if (!dataTable) return;\n",
1222
       "\n",
1223
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
1224
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1225
       "            + ' to learn more about interactive tables.';\n",
1226
       "          element.innerHTML = '';\n",
1227
       "          dataTable['output_type'] = 'display_data';\n",
1228
       "          await google.colab.output.renderOutput(dataTable, element);\n",
1229
       "          const docLink = document.createElement('div');\n",
1230
       "          docLink.innerHTML = docLinkHtml;\n",
1231
       "          element.appendChild(docLink);\n",
1232
       "        }\n",
1233
       "      </script>\n",
1234
       "    </div>\n",
1235
       "  </div>\n",
1236
       "  "
1237
      ],
1238
      "text/plain": [
1239
       "             Sales\n",
1240
       "Month             \n",
1241
       "1964-01-01  2815.0\n",
1242
       "1964-02-01  2672.0\n",
1243
       "1964-03-01  2755.0\n",
1244
       "1964-04-01  2721.0\n",
1245
       "1964-05-01  2946.0"
1246
      ]
1247
     },
1248
     "execution_count": 16,
1249
     "metadata": {},
1250
     "output_type": "execute_result"
1251
    }
1252
   ],
1253
   "source": [
1254
    "df.head()"
1255
   ]
1256
  },
1257
  {
1258
   "cell_type": "code",
1259
   "execution_count": 17,
1260
   "metadata": {
1261
    "colab": {
1262
     "base_uri": "https://localhost:8080/",
1263
     "height": 300
1264
    },
1265
    "id": "uEO_U2e5X3SF",
1266
    "outputId": "9ee02bda-cf4f-4ea3-eec3-014b90323ea5"
1267
   },
1268
   "outputs": [
1269
    {
1270
     "data": {
1271
      "text/html": [
1272
       "\n",
1273
       "  <div id=\"df-e9ca0501-7174-462e-b7b1-724e2faee828\">\n",
1274
       "    <div class=\"colab-df-container\">\n",
1275
       "      <div>\n",
1276
       "<style scoped>\n",
1277
       "    .dataframe tbody tr th:only-of-type {\n",
1278
       "        vertical-align: middle;\n",
1279
       "    }\n",
1280
       "\n",
1281
       "    .dataframe tbody tr th {\n",
1282
       "        vertical-align: top;\n",
1283
       "    }\n",
1284
       "\n",
1285
       "    .dataframe thead th {\n",
1286
       "        text-align: right;\n",
1287
       "    }\n",
1288
       "</style>\n",
1289
       "<table border=\"1\" class=\"dataframe\">\n",
1290
       "  <thead>\n",
1291
       "    <tr style=\"text-align: right;\">\n",
1292
       "      <th></th>\n",
1293
       "      <th>Sales</th>\n",
1294
       "    </tr>\n",
1295
       "  </thead>\n",
1296
       "  <tbody>\n",
1297
       "    <tr>\n",
1298
       "      <th>count</th>\n",
1299
       "      <td>105.000000</td>\n",
1300
       "    </tr>\n",
1301
       "    <tr>\n",
1302
       "      <th>mean</th>\n",
1303
       "      <td>4761.152381</td>\n",
1304
       "    </tr>\n",
1305
       "    <tr>\n",
1306
       "      <th>std</th>\n",
1307
       "      <td>2553.502601</td>\n",
1308
       "    </tr>\n",
1309
       "    <tr>\n",
1310
       "      <th>min</th>\n",
1311
       "      <td>1413.000000</td>\n",
1312
       "    </tr>\n",
1313
       "    <tr>\n",
1314
       "      <th>25%</th>\n",
1315
       "      <td>3113.000000</td>\n",
1316
       "    </tr>\n",
1317
       "    <tr>\n",
1318
       "      <th>50%</th>\n",
1319
       "      <td>4217.000000</td>\n",
1320
       "    </tr>\n",
1321
       "    <tr>\n",
1322
       "      <th>75%</th>\n",
1323
       "      <td>5221.000000</td>\n",
1324
       "    </tr>\n",
1325
       "    <tr>\n",
1326
       "      <th>max</th>\n",
1327
       "      <td>13916.000000</td>\n",
1328
       "    </tr>\n",
1329
       "  </tbody>\n",
1330
       "</table>\n",
1331
       "</div>\n",
1332
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9ca0501-7174-462e-b7b1-724e2faee828')\"\n",
1333
       "              title=\"Convert this dataframe to an interactive table.\"\n",
1334
       "              style=\"display:none;\">\n",
1335
       "        \n",
1336
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1337
       "       width=\"24px\">\n",
1338
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
1339
       "    <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",
1340
       "  </svg>\n",
1341
       "      </button>\n",
1342
       "      \n",
1343
       "  <style>\n",
1344
       "    .colab-df-container {\n",
1345
       "      display:flex;\n",
1346
       "      flex-wrap:wrap;\n",
1347
       "      gap: 12px;\n",
1348
       "    }\n",
1349
       "\n",
1350
       "    .colab-df-convert {\n",
1351
       "      background-color: #E8F0FE;\n",
1352
       "      border: none;\n",
1353
       "      border-radius: 50%;\n",
1354
       "      cursor: pointer;\n",
1355
       "      display: none;\n",
1356
       "      fill: #1967D2;\n",
1357
       "      height: 32px;\n",
1358
       "      padding: 0 0 0 0;\n",
1359
       "      width: 32px;\n",
1360
       "    }\n",
1361
       "\n",
1362
       "    .colab-df-convert:hover {\n",
1363
       "      background-color: #E2EBFA;\n",
1364
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1365
       "      fill: #174EA6;\n",
1366
       "    }\n",
1367
       "\n",
1368
       "    [theme=dark] .colab-df-convert {\n",
1369
       "      background-color: #3B4455;\n",
1370
       "      fill: #D2E3FC;\n",
1371
       "    }\n",
1372
       "\n",
1373
       "    [theme=dark] .colab-df-convert:hover {\n",
1374
       "      background-color: #434B5C;\n",
1375
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1376
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1377
       "      fill: #FFFFFF;\n",
1378
       "    }\n",
1379
       "  </style>\n",
1380
       "\n",
1381
       "      <script>\n",
1382
       "        const buttonEl =\n",
1383
       "          document.querySelector('#df-e9ca0501-7174-462e-b7b1-724e2faee828 button.colab-df-convert');\n",
1384
       "        buttonEl.style.display =\n",
1385
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1386
       "\n",
1387
       "        async function convertToInteractive(key) {\n",
1388
       "          const element = document.querySelector('#df-e9ca0501-7174-462e-b7b1-724e2faee828');\n",
1389
       "          const dataTable =\n",
1390
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1391
       "                                                     [key], {});\n",
1392
       "          if (!dataTable) return;\n",
1393
       "\n",
1394
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
1395
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1396
       "            + ' to learn more about interactive tables.';\n",
1397
       "          element.innerHTML = '';\n",
1398
       "          dataTable['output_type'] = 'display_data';\n",
1399
       "          await google.colab.output.renderOutput(dataTable, element);\n",
1400
       "          const docLink = document.createElement('div');\n",
1401
       "          docLink.innerHTML = docLinkHtml;\n",
1402
       "          element.appendChild(docLink);\n",
1403
       "        }\n",
1404
       "      </script>\n",
1405
       "    </div>\n",
1406
       "  </div>\n",
1407
       "  "
1408
      ],
1409
      "text/plain": [
1410
       "              Sales\n",
1411
       "count    105.000000\n",
1412
       "mean    4761.152381\n",
1413
       "std     2553.502601\n",
1414
       "min     1413.000000\n",
1415
       "25%     3113.000000\n",
1416
       "50%     4217.000000\n",
1417
       "75%     5221.000000\n",
1418
       "max    13916.000000"
1419
      ]
1420
     },
1421
     "execution_count": 17,
1422
     "metadata": {},
1423
     "output_type": "execute_result"
1424
    }
1425
   ],
1426
   "source": [
1427
    "df.describe()"
1428
   ]
1429
  },
1430
  {
1431
   "cell_type": "markdown",
1432
   "metadata": {
1433
    "id": "bSKUYa0hX3SF"
1434
   },
1435
   "source": [
1436
    "## Step 2: Visualize the Data"
1437
   ]
1438
  },
1439
  {
1440
   "cell_type": "code",
1441
   "execution_count": 18,
1442
   "metadata": {
1443
    "colab": {
1444
     "base_uri": "https://localhost:8080/",
1445
     "height": 466
1446
    },
1447
    "id": "Zn20eFD5X3SG",
1448
    "outputId": "2d797cd8-e7ed-41d5-8cdc-962e14176d94"
1449
   },
1450
   "outputs": [
1451
    {
1452
     "data": {
1453
      "text/plain": [
1454
       "<Axes: xlabel='Month'>"
1455
      ]
1456
     },
1457
     "execution_count": 18,
1458
     "metadata": {},
1459
     "output_type": "execute_result"
1460
    },
1461
    {
1462
     "data": {
1463
      "image/png": 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\n",
1464
      "text/plain": [
1465
       "<Figure size 640x480 with 1 Axes>"
1466
      ]
1467
     },
1468
     "metadata": {},
1469
     "output_type": "display_data"
1470
    }
1471
   ],
1472
   "source": [
1473
    "df.plot()"
1474
   ]
1475
  },
1476
  {
1477
   "cell_type": "code",
1478
   "execution_count": 19,
1479
   "metadata": {
1480
    "id": "Yty0H4FkX3SG"
1481
   },
1482
   "outputs": [],
1483
   "source": [
1484
    "#looking this graph,we can say this data is seasonal\n",
1485
    "#(seasonal is all about suppose in each yr in christmas sales goes up) and then down"
1486
   ]
1487
  },
1488
  {
1489
   "cell_type": "code",
1490
   "execution_count": 20,
1491
   "metadata": {
1492
    "id": "FtOC8LaqX3SH"
1493
   },
1494
   "outputs": [],
1495
   "source": [
1496
    "# we plot,whether test is stationary or not\n",
1497
    "#if not stationery then how to make it stationery"
1498
   ]
1499
  },
1500
  {
1501
   "cell_type": "code",
1502
   "execution_count": 21,
1503
   "metadata": {
1504
    "id": "QuDdn01uX3SH"
1505
   },
1506
   "outputs": [],
1507
   "source": [
1508
    "### Testing For Stationarity\n",
1509
    "\n",
1510
    "from statsmodels.tsa.stattools import adfuller"
1511
   ]
1512
  },
1513
  {
1514
   "cell_type": "code",
1515
   "execution_count": 22,
1516
   "metadata": {
1517
    "id": "4FYWzLQWX3SH"
1518
   },
1519
   "outputs": [],
1520
   "source": [
1521
    "test_result=adfuller(df['Sales'])"
1522
   ]
1523
  },
1524
  {
1525
   "cell_type": "code",
1526
   "execution_count": 23,
1527
   "metadata": {
1528
    "id": "1IupXiGYX3SH"
1529
   },
1530
   "outputs": [],
1531
   "source": [
1532
    "#adfuller gives 5 values ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
1533
    "#most imp is p-value\n",
1534
    "#this is almost similar to Hypothesis testing,whereas Null hypo which says Data is not Stationery\n",
1535
    "#whereas Alternate hypo says data is stationery\n",
1536
    "\n"
1537
   ]
1538
  },
1539
  {
1540
   "cell_type": "code",
1541
   "execution_count": 24,
1542
   "metadata": {
1543
    "id": "QYwH9FrWX3SH"
1544
   },
1545
   "outputs": [],
1546
   "source": [
1547
    "###if Pvalue<0.05 ,we reject Null hypo ie alternate hypo is true,ie data is stationery"
1548
   ]
1549
  },
1550
  {
1551
   "cell_type": "code",
1552
   "execution_count": 25,
1553
   "metadata": {
1554
    "id": "y63Yx-CmX3SI"
1555
   },
1556
   "outputs": [],
1557
   "source": [
1558
    "\n",
1559
    "#zip basically combines result,labels\n",
1560
    "\n",
1561
    "def adfuller_test(sales):\n",
1562
    "    result=adfuller(sales)\n",
1563
    "    labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
1564
    "    for value,label in zip(result,labels):\n",
1565
    "        print(label+' : '+str(value) )\n",
1566
    "    if result[1] <= 0.05:\n",
1567
    "        print(\"strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\")\n",
1568
    "    else:\n",
1569
    "        print(\"weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \")\n"
1570
   ]
1571
  },
1572
  {
1573
   "cell_type": "code",
1574
   "execution_count": 26,
1575
   "metadata": {
1576
    "colab": {
1577
     "base_uri": "https://localhost:8080/"
1578
    },
1579
    "id": "gvDfY7mGX3SI",
1580
    "outputId": "48d1a6b1-1330-4af4-8185-179ed40ddcf8"
1581
   },
1582
   "outputs": [
1583
    {
1584
     "name": "stdout",
1585
     "output_type": "stream",
1586
     "text": [
1587
      "ADF Test Statistic : -1.8335930563276215\n",
1588
      "p-value : 0.36391577166024586\n",
1589
      "#Lags Used : 11\n",
1590
      "Number of Observations Used : 93\n",
1591
      "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n"
1592
     ]
1593
    }
1594
   ],
1595
   "source": [
1596
    "adfuller_test(df['Sales'])"
1597
   ]
1598
  },
1599
  {
1600
   "cell_type": "code",
1601
   "execution_count": 27,
1602
   "metadata": {
1603
    "id": "NKFUpp6BX3SI"
1604
   },
1605
   "outputs": [],
1606
   "source": [
1607
    "#if our data is non-stationery,we have to make it stationery using various techniques such as Differencing & many more"
1608
   ]
1609
  },
1610
  {
1611
   "cell_type": "markdown",
1612
   "metadata": {
1613
    "id": "c8UJDsNKX3SI"
1614
   },
1615
   "source": [
1616
    "## Differencing"
1617
   ]
1618
  },
1619
  {
1620
   "cell_type": "code",
1621
   "execution_count": 28,
1622
   "metadata": {
1623
    "colab": {
1624
     "base_uri": "https://localhost:8080/",
1625
     "height": 455
1626
    },
1627
    "id": "ieuHzOfhX3SI",
1628
    "outputId": "c56d995c-aeae-42b5-bed1-81d6a240466c"
1629
   },
1630
   "outputs": [
1631
    {
1632
     "data": {
1633
      "text/html": [
1634
       "\n",
1635
       "  <div id=\"df-66cfe8e4-b8a3-4f99-aa09-e5d39680e264\">\n",
1636
       "    <div class=\"colab-df-container\">\n",
1637
       "      <div>\n",
1638
       "<style scoped>\n",
1639
       "    .dataframe tbody tr th:only-of-type {\n",
1640
       "        vertical-align: middle;\n",
1641
       "    }\n",
1642
       "\n",
1643
       "    .dataframe tbody tr th {\n",
1644
       "        vertical-align: top;\n",
1645
       "    }\n",
1646
       "\n",
1647
       "    .dataframe thead th {\n",
1648
       "        text-align: right;\n",
1649
       "    }\n",
1650
       "</style>\n",
1651
       "<table border=\"1\" class=\"dataframe\">\n",
1652
       "  <thead>\n",
1653
       "    <tr style=\"text-align: right;\">\n",
1654
       "      <th></th>\n",
1655
       "      <th>Sales</th>\n",
1656
       "    </tr>\n",
1657
       "    <tr>\n",
1658
       "      <th>Month</th>\n",
1659
       "      <th></th>\n",
1660
       "    </tr>\n",
1661
       "  </thead>\n",
1662
       "  <tbody>\n",
1663
       "    <tr>\n",
1664
       "      <th>1964-01-01</th>\n",
1665
       "      <td>2815.0</td>\n",
1666
       "    </tr>\n",
1667
       "    <tr>\n",
1668
       "      <th>1964-02-01</th>\n",
1669
       "      <td>2672.0</td>\n",
1670
       "    </tr>\n",
1671
       "    <tr>\n",
1672
       "      <th>1964-03-01</th>\n",
1673
       "      <td>2755.0</td>\n",
1674
       "    </tr>\n",
1675
       "    <tr>\n",
1676
       "      <th>1964-04-01</th>\n",
1677
       "      <td>2721.0</td>\n",
1678
       "    </tr>\n",
1679
       "    <tr>\n",
1680
       "      <th>1964-05-01</th>\n",
1681
       "      <td>2946.0</td>\n",
1682
       "    </tr>\n",
1683
       "    <tr>\n",
1684
       "      <th>...</th>\n",
1685
       "      <td>...</td>\n",
1686
       "    </tr>\n",
1687
       "    <tr>\n",
1688
       "      <th>1972-05-01</th>\n",
1689
       "      <td>4618.0</td>\n",
1690
       "    </tr>\n",
1691
       "    <tr>\n",
1692
       "      <th>1972-06-01</th>\n",
1693
       "      <td>5312.0</td>\n",
1694
       "    </tr>\n",
1695
       "    <tr>\n",
1696
       "      <th>1972-07-01</th>\n",
1697
       "      <td>4298.0</td>\n",
1698
       "    </tr>\n",
1699
       "    <tr>\n",
1700
       "      <th>1972-08-01</th>\n",
1701
       "      <td>1413.0</td>\n",
1702
       "    </tr>\n",
1703
       "    <tr>\n",
1704
       "      <th>1972-09-01</th>\n",
1705
       "      <td>5877.0</td>\n",
1706
       "    </tr>\n",
1707
       "  </tbody>\n",
1708
       "</table>\n",
1709
       "<p>105 rows × 1 columns</p>\n",
1710
       "</div>\n",
1711
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-66cfe8e4-b8a3-4f99-aa09-e5d39680e264')\"\n",
1712
       "              title=\"Convert this dataframe to an interactive table.\"\n",
1713
       "              style=\"display:none;\">\n",
1714
       "        \n",
1715
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1716
       "       width=\"24px\">\n",
1717
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
1718
       "    <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",
1719
       "  </svg>\n",
1720
       "      </button>\n",
1721
       "      \n",
1722
       "  <style>\n",
1723
       "    .colab-df-container {\n",
1724
       "      display:flex;\n",
1725
       "      flex-wrap:wrap;\n",
1726
       "      gap: 12px;\n",
1727
       "    }\n",
1728
       "\n",
1729
       "    .colab-df-convert {\n",
1730
       "      background-color: #E8F0FE;\n",
1731
       "      border: none;\n",
1732
       "      border-radius: 50%;\n",
1733
       "      cursor: pointer;\n",
1734
       "      display: none;\n",
1735
       "      fill: #1967D2;\n",
1736
       "      height: 32px;\n",
1737
       "      padding: 0 0 0 0;\n",
1738
       "      width: 32px;\n",
1739
       "    }\n",
1740
       "\n",
1741
       "    .colab-df-convert:hover {\n",
1742
       "      background-color: #E2EBFA;\n",
1743
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1744
       "      fill: #174EA6;\n",
1745
       "    }\n",
1746
       "\n",
1747
       "    [theme=dark] .colab-df-convert {\n",
1748
       "      background-color: #3B4455;\n",
1749
       "      fill: #D2E3FC;\n",
1750
       "    }\n",
1751
       "\n",
1752
       "    [theme=dark] .colab-df-convert:hover {\n",
1753
       "      background-color: #434B5C;\n",
1754
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1755
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1756
       "      fill: #FFFFFF;\n",
1757
       "    }\n",
1758
       "  </style>\n",
1759
       "\n",
1760
       "      <script>\n",
1761
       "        const buttonEl =\n",
1762
       "          document.querySelector('#df-66cfe8e4-b8a3-4f99-aa09-e5d39680e264 button.colab-df-convert');\n",
1763
       "        buttonEl.style.display =\n",
1764
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1765
       "\n",
1766
       "        async function convertToInteractive(key) {\n",
1767
       "          const element = document.querySelector('#df-66cfe8e4-b8a3-4f99-aa09-e5d39680e264');\n",
1768
       "          const dataTable =\n",
1769
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1770
       "                                                     [key], {});\n",
1771
       "          if (!dataTable) return;\n",
1772
       "\n",
1773
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
1774
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1775
       "            + ' to learn more about interactive tables.';\n",
1776
       "          element.innerHTML = '';\n",
1777
       "          dataTable['output_type'] = 'display_data';\n",
1778
       "          await google.colab.output.renderOutput(dataTable, element);\n",
1779
       "          const docLink = document.createElement('div');\n",
1780
       "          docLink.innerHTML = docLinkHtml;\n",
1781
       "          element.appendChild(docLink);\n",
1782
       "        }\n",
1783
       "      </script>\n",
1784
       "    </div>\n",
1785
       "  </div>\n",
1786
       "  "
1787
      ],
1788
      "text/plain": [
1789
       "             Sales\n",
1790
       "Month             \n",
1791
       "1964-01-01  2815.0\n",
1792
       "1964-02-01  2672.0\n",
1793
       "1964-03-01  2755.0\n",
1794
       "1964-04-01  2721.0\n",
1795
       "1964-05-01  2946.0\n",
1796
       "...            ...\n",
1797
       "1972-05-01  4618.0\n",
1798
       "1972-06-01  5312.0\n",
1799
       "1972-07-01  4298.0\n",
1800
       "1972-08-01  1413.0\n",
1801
       "1972-09-01  5877.0\n",
1802
       "\n",
1803
       "[105 rows x 1 columns]"
1804
      ]
1805
     },
1806
     "execution_count": 28,
1807
     "metadata": {},
1808
     "output_type": "execute_result"
1809
    }
1810
   ],
1811
   "source": [
1812
    "df"
1813
   ]
1814
  },
1815
  {
1816
   "cell_type": "code",
1817
   "execution_count": 29,
1818
   "metadata": {
1819
    "colab": {
1820
     "base_uri": "https://localhost:8080/"
1821
    },
1822
    "id": "CwYzs1c2X3SI",
1823
    "outputId": "e6ca8b88-bd22-4779-83df-27a1df51f197"
1824
   },
1825
   "outputs": [
1826
    {
1827
     "data": {
1828
      "text/plain": [
1829
       "Month\n",
1830
       "1964-01-01       NaN\n",
1831
       "1964-02-01    2815.0\n",
1832
       "1964-03-01    2672.0\n",
1833
       "1964-04-01    2755.0\n",
1834
       "1964-05-01    2721.0\n",
1835
       "               ...  \n",
1836
       "1972-05-01    4788.0\n",
1837
       "1972-06-01    4618.0\n",
1838
       "1972-07-01    5312.0\n",
1839
       "1972-08-01    4298.0\n",
1840
       "1972-09-01    1413.0\n",
1841
       "Name: Sales, Length: 105, dtype: float64"
1842
      ]
1843
     },
1844
     "execution_count": 29,
1845
     "metadata": {},
1846
     "output_type": "execute_result"
1847
    }
1848
   ],
1849
   "source": [
1850
    "df['Sales'].shift(1)"
1851
   ]
1852
  },
1853
  {
1854
   "cell_type": "code",
1855
   "execution_count": 30,
1856
   "metadata": {
1857
    "id": "3DX_BVycX3SJ"
1858
   },
1859
   "outputs": [],
1860
   "source": [
1861
    "df['Sales First Difference'] = df['Sales'] - df['Sales'].shift(1)"
1862
   ]
1863
  },
1864
  {
1865
   "cell_type": "code",
1866
   "execution_count": 31,
1867
   "metadata": {
1868
    "id": "Xyn-6_SjX3SJ"
1869
   },
1870
   "outputs": [],
1871
   "source": [
1872
    "#why taken shift(12),bcz basically year has 12 month cycle\n",
1873
    "df['Seasonal First Difference']=df['Sales']-df['Sales'].shift(12)"
1874
   ]
1875
  },
1876
  {
1877
   "cell_type": "code",
1878
   "execution_count": 32,
1879
   "metadata": {
1880
    "colab": {
1881
     "base_uri": "https://localhost:8080/",
1882
     "height": 520
1883
    },
1884
    "id": "bXAbv9Z9X3SJ",
1885
    "outputId": "af00ca1f-d416-44e6-971c-85f5227d5041"
1886
   },
1887
   "outputs": [
1888
    {
1889
     "data": {
1890
      "text/html": [
1891
       "\n",
1892
       "  <div id=\"df-d278a5ec-6a4c-47e9-9ae8-4abb76835588\">\n",
1893
       "    <div class=\"colab-df-container\">\n",
1894
       "      <div>\n",
1895
       "<style scoped>\n",
1896
       "    .dataframe tbody tr th:only-of-type {\n",
1897
       "        vertical-align: middle;\n",
1898
       "    }\n",
1899
       "\n",
1900
       "    .dataframe tbody tr th {\n",
1901
       "        vertical-align: top;\n",
1902
       "    }\n",
1903
       "\n",
1904
       "    .dataframe thead th {\n",
1905
       "        text-align: right;\n",
1906
       "    }\n",
1907
       "</style>\n",
1908
       "<table border=\"1\" class=\"dataframe\">\n",
1909
       "  <thead>\n",
1910
       "    <tr style=\"text-align: right;\">\n",
1911
       "      <th></th>\n",
1912
       "      <th>Sales</th>\n",
1913
       "      <th>Sales First Difference</th>\n",
1914
       "      <th>Seasonal First Difference</th>\n",
1915
       "    </tr>\n",
1916
       "    <tr>\n",
1917
       "      <th>Month</th>\n",
1918
       "      <th></th>\n",
1919
       "      <th></th>\n",
1920
       "      <th></th>\n",
1921
       "    </tr>\n",
1922
       "  </thead>\n",
1923
       "  <tbody>\n",
1924
       "    <tr>\n",
1925
       "      <th>1964-01-01</th>\n",
1926
       "      <td>2815.0</td>\n",
1927
       "      <td>NaN</td>\n",
1928
       "      <td>NaN</td>\n",
1929
       "    </tr>\n",
1930
       "    <tr>\n",
1931
       "      <th>1964-02-01</th>\n",
1932
       "      <td>2672.0</td>\n",
1933
       "      <td>-143.0</td>\n",
1934
       "      <td>NaN</td>\n",
1935
       "    </tr>\n",
1936
       "    <tr>\n",
1937
       "      <th>1964-03-01</th>\n",
1938
       "      <td>2755.0</td>\n",
1939
       "      <td>83.0</td>\n",
1940
       "      <td>NaN</td>\n",
1941
       "    </tr>\n",
1942
       "    <tr>\n",
1943
       "      <th>1964-04-01</th>\n",
1944
       "      <td>2721.0</td>\n",
1945
       "      <td>-34.0</td>\n",
1946
       "      <td>NaN</td>\n",
1947
       "    </tr>\n",
1948
       "    <tr>\n",
1949
       "      <th>1964-05-01</th>\n",
1950
       "      <td>2946.0</td>\n",
1951
       "      <td>225.0</td>\n",
1952
       "      <td>NaN</td>\n",
1953
       "    </tr>\n",
1954
       "    <tr>\n",
1955
       "      <th>1964-06-01</th>\n",
1956
       "      <td>3036.0</td>\n",
1957
       "      <td>90.0</td>\n",
1958
       "      <td>NaN</td>\n",
1959
       "    </tr>\n",
1960
       "    <tr>\n",
1961
       "      <th>1964-07-01</th>\n",
1962
       "      <td>2282.0</td>\n",
1963
       "      <td>-754.0</td>\n",
1964
       "      <td>NaN</td>\n",
1965
       "    </tr>\n",
1966
       "    <tr>\n",
1967
       "      <th>1964-08-01</th>\n",
1968
       "      <td>2212.0</td>\n",
1969
       "      <td>-70.0</td>\n",
1970
       "      <td>NaN</td>\n",
1971
       "    </tr>\n",
1972
       "    <tr>\n",
1973
       "      <th>1964-09-01</th>\n",
1974
       "      <td>2922.0</td>\n",
1975
       "      <td>710.0</td>\n",
1976
       "      <td>NaN</td>\n",
1977
       "    </tr>\n",
1978
       "    <tr>\n",
1979
       "      <th>1964-10-01</th>\n",
1980
       "      <td>4301.0</td>\n",
1981
       "      <td>1379.0</td>\n",
1982
       "      <td>NaN</td>\n",
1983
       "    </tr>\n",
1984
       "    <tr>\n",
1985
       "      <th>1964-11-01</th>\n",
1986
       "      <td>5764.0</td>\n",
1987
       "      <td>1463.0</td>\n",
1988
       "      <td>NaN</td>\n",
1989
       "    </tr>\n",
1990
       "    <tr>\n",
1991
       "      <th>1964-12-01</th>\n",
1992
       "      <td>7312.0</td>\n",
1993
       "      <td>1548.0</td>\n",
1994
       "      <td>NaN</td>\n",
1995
       "    </tr>\n",
1996
       "    <tr>\n",
1997
       "      <th>1965-01-01</th>\n",
1998
       "      <td>2541.0</td>\n",
1999
       "      <td>-4771.0</td>\n",
2000
       "      <td>-274.0</td>\n",
2001
       "    </tr>\n",
2002
       "    <tr>\n",
2003
       "      <th>1965-02-01</th>\n",
2004
       "      <td>2475.0</td>\n",
2005
       "      <td>-66.0</td>\n",
2006
       "      <td>-197.0</td>\n",
2007
       "    </tr>\n",
2008
       "  </tbody>\n",
2009
       "</table>\n",
2010
       "</div>\n",
2011
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d278a5ec-6a4c-47e9-9ae8-4abb76835588')\"\n",
2012
       "              title=\"Convert this dataframe to an interactive table.\"\n",
2013
       "              style=\"display:none;\">\n",
2014
       "        \n",
2015
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
2016
       "       width=\"24px\">\n",
2017
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
2018
       "    <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",
2019
       "  </svg>\n",
2020
       "      </button>\n",
2021
       "      \n",
2022
       "  <style>\n",
2023
       "    .colab-df-container {\n",
2024
       "      display:flex;\n",
2025
       "      flex-wrap:wrap;\n",
2026
       "      gap: 12px;\n",
2027
       "    }\n",
2028
       "\n",
2029
       "    .colab-df-convert {\n",
2030
       "      background-color: #E8F0FE;\n",
2031
       "      border: none;\n",
2032
       "      border-radius: 50%;\n",
2033
       "      cursor: pointer;\n",
2034
       "      display: none;\n",
2035
       "      fill: #1967D2;\n",
2036
       "      height: 32px;\n",
2037
       "      padding: 0 0 0 0;\n",
2038
       "      width: 32px;\n",
2039
       "    }\n",
2040
       "\n",
2041
       "    .colab-df-convert:hover {\n",
2042
       "      background-color: #E2EBFA;\n",
2043
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
2044
       "      fill: #174EA6;\n",
2045
       "    }\n",
2046
       "\n",
2047
       "    [theme=dark] .colab-df-convert {\n",
2048
       "      background-color: #3B4455;\n",
2049
       "      fill: #D2E3FC;\n",
2050
       "    }\n",
2051
       "\n",
2052
       "    [theme=dark] .colab-df-convert:hover {\n",
2053
       "      background-color: #434B5C;\n",
2054
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
2055
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
2056
       "      fill: #FFFFFF;\n",
2057
       "    }\n",
2058
       "  </style>\n",
2059
       "\n",
2060
       "      <script>\n",
2061
       "        const buttonEl =\n",
2062
       "          document.querySelector('#df-d278a5ec-6a4c-47e9-9ae8-4abb76835588 button.colab-df-convert');\n",
2063
       "        buttonEl.style.display =\n",
2064
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
2065
       "\n",
2066
       "        async function convertToInteractive(key) {\n",
2067
       "          const element = document.querySelector('#df-d278a5ec-6a4c-47e9-9ae8-4abb76835588');\n",
2068
       "          const dataTable =\n",
2069
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
2070
       "                                                     [key], {});\n",
2071
       "          if (!dataTable) return;\n",
2072
       "\n",
2073
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
2074
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
2075
       "            + ' to learn more about interactive tables.';\n",
2076
       "          element.innerHTML = '';\n",
2077
       "          dataTable['output_type'] = 'display_data';\n",
2078
       "          await google.colab.output.renderOutput(dataTable, element);\n",
2079
       "          const docLink = document.createElement('div');\n",
2080
       "          docLink.innerHTML = docLinkHtml;\n",
2081
       "          element.appendChild(docLink);\n",
2082
       "        }\n",
2083
       "      </script>\n",
2084
       "    </div>\n",
2085
       "  </div>\n",
2086
       "  "
2087
      ],
2088
      "text/plain": [
2089
       "             Sales  Sales First Difference  Seasonal First Difference\n",
2090
       "Month                                                                \n",
2091
       "1964-01-01  2815.0                     NaN                        NaN\n",
2092
       "1964-02-01  2672.0                  -143.0                        NaN\n",
2093
       "1964-03-01  2755.0                    83.0                        NaN\n",
2094
       "1964-04-01  2721.0                   -34.0                        NaN\n",
2095
       "1964-05-01  2946.0                   225.0                        NaN\n",
2096
       "1964-06-01  3036.0                    90.0                        NaN\n",
2097
       "1964-07-01  2282.0                  -754.0                        NaN\n",
2098
       "1964-08-01  2212.0                   -70.0                        NaN\n",
2099
       "1964-09-01  2922.0                   710.0                        NaN\n",
2100
       "1964-10-01  4301.0                  1379.0                        NaN\n",
2101
       "1964-11-01  5764.0                  1463.0                        NaN\n",
2102
       "1964-12-01  7312.0                  1548.0                        NaN\n",
2103
       "1965-01-01  2541.0                 -4771.0                     -274.0\n",
2104
       "1965-02-01  2475.0                   -66.0                     -197.0"
2105
      ]
2106
     },
2107
     "execution_count": 32,
2108
     "metadata": {},
2109
     "output_type": "execute_result"
2110
    }
2111
   ],
2112
   "source": [
2113
    "df.head(14)"
2114
   ]
2115
  },
2116
  {
2117
   "cell_type": "code",
2118
   "execution_count": 33,
2119
   "metadata": {
2120
    "colab": {
2121
     "base_uri": "https://localhost:8080/"
2122
    },
2123
    "id": "vvK8GjR1X3SJ",
2124
    "outputId": "2d2f4a6b-b980-46f3-8b2f-07cd7a01e2fb"
2125
   },
2126
   "outputs": [
2127
    {
2128
     "name": "stdout",
2129
     "output_type": "stream",
2130
     "text": [
2131
      "ADF Test Statistic : -7.626619157213166\n",
2132
      "p-value : 2.0605796968136632e-11\n",
2133
      "#Lags Used : 0\n",
2134
      "Number of Observations Used : 92\n",
2135
      "strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\n"
2136
     ]
2137
    }
2138
   ],
2139
   "source": [
2140
    "## Again test dickey fuller test on df['Sales First Difference']\n",
2141
    "adfuller_test(df['Seasonal First Difference'].dropna())"
2142
   ]
2143
  },
2144
  {
2145
   "cell_type": "code",
2146
   "execution_count": 34,
2147
   "metadata": {
2148
    "id": "N6YcpHR_X3SJ"
2149
   },
2150
   "outputs": [],
2151
   "source": [
2152
    "#now our p-value is less than 0.05 which basically says we are rejecting null hypo and accepting alternate hypo\n",
2153
    "#ie data is stationery\n",
2154
    "#if p-value is almost 0,then we have a wonderful stationery graph"
2155
   ]
2156
  },
2157
  {
2158
   "cell_type": "code",
2159
   "execution_count": 35,
2160
   "metadata": {
2161
    "colab": {
2162
     "base_uri": "https://localhost:8080/",
2163
     "height": 466
2164
    },
2165
    "id": "oNqUrObFX3SJ",
2166
    "outputId": "97963fe1-4e23-4626-d01f-a43b5a5494e4"
2167
   },
2168
   "outputs": [
2169
    {
2170
     "data": {
2171
      "text/plain": [
2172
       "<Axes: xlabel='Month'>"
2173
      ]
2174
     },
2175
     "execution_count": 35,
2176
     "metadata": {},
2177
     "output_type": "execute_result"
2178
    },
2179
    {
2180
     "data": {
2181
      "image/png": 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\n",
2182
      "text/plain": [
2183
       "<Figure size 640x480 with 1 Axes>"
2184
      ]
2185
     },
2186
     "metadata": {},
2187
     "output_type": "display_data"
2188
    }
2189
   ],
2190
   "source": [
2191
    "df['Seasonal First Difference'].plot()"
2192
   ]
2193
  },
2194
  {
2195
   "cell_type": "code",
2196
   "execution_count": 36,
2197
   "metadata": {
2198
    "id": "PofdXvysX3SJ"
2199
   },
2200
   "outputs": [],
2201
   "source": [
2202
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
2203
    "#Autocorrelation(plot_acf),Partial Autocorrelation(plot_pacf)"
2204
   ]
2205
  },
2206
  {
2207
   "cell_type": "code",
2208
   "execution_count": 37,
2209
   "metadata": {
2210
    "id": "ZKx9XbtrX3SM"
2211
   },
2212
   "outputs": [],
2213
   "source": [
2214
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf"
2215
   ]
2216
  },
2217
  {
2218
   "cell_type": "code",
2219
   "execution_count": 38,
2220
   "metadata": {
2221
    "colab": {
2222
     "base_uri": "https://localhost:8080/",
2223
     "height": 754
2224
    },
2225
    "id": "Non9zrjOX3SM",
2226
    "outputId": "7225e922-446b-4406-dfc9-ecb42adcea45"
2227
   },
2228
   "outputs": [
2229
    {
2230
     "name": "stderr",
2231
     "output_type": "stream",
2232
     "text": [
2233
      "/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
2234
      "  warnings.warn(\n"
2235
     ]
2236
    },
2237
    {
2238
     "data": {
2239
      "image/png": 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\n",
2240
      "text/plain": [
2241
       "<Figure size 1200x800 with 2 Axes>"
2242
      ]
2243
     },
2244
     "metadata": {},
2245
     "output_type": "display_data"
2246
    }
2247
   ],
2248
   "source": [
2249
    "\n",
2250
    "fig = plt.figure(figsize=(12,8))\n",
2251
    "ax1 = fig.add_subplot(211)\n",
2252
    "fig = plot_acf(df['Seasonal First Difference'].iloc[13:],lags=40,ax=ax1)\n",
2253
    "ax2 = fig.add_subplot(212)\n",
2254
    "fig = plot_pacf(df['Seasonal First Difference'].iloc[13:],lags=40,ax=ax2)"
2255
   ]
2256
  },
2257
  {
2258
   "cell_type": "code",
2259
   "execution_count": 39,
2260
   "metadata": {
2261
    "id": "6d9uiKdQX3SQ"
2262
   },
2263
   "outputs": [],
2264
   "source": [
2265
    "# For non-seasonal data\n",
2266
    "#p=1, d=1, q=0 or 1\n",
2267
    "from statsmodels.tsa.arima_model import ARIMA"
2268
   ]
2269
  },
2270
  {
2271
   "cell_type": "code",
2272
   "execution_count": 41,
2273
   "metadata": {
2274
    "colab": {
2275
     "base_uri": "https://localhost:8080/"
2276
    },
2277
    "id": "-LlmYpjfX3SQ",
2278
    "outputId": "6ed8fa3c-2eb8-47c1-d1ad-ceeb7ec1a9f6"
2279
   },
2280
   "outputs": [
2281
    {
2282
     "name": "stderr",
2283
     "output_type": "stream",
2284
     "text": [
2285
      "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
2286
      "  self._init_dates(dates, freq)\n",
2287
      "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
2288
      "  self._init_dates(dates, freq)\n",
2289
      "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
2290
      "  self._init_dates(dates, freq)\n"
2291
     ]
2292
    }
2293
   ],
2294
   "source": [
2295
    "from statsmodels.tsa.arima.model import ARIMA\n",
2296
    "model = ARIMA(df['Sales'], order=(1, 1, 1))\n",
2297
    "model_fit = model.fit()\n"
2298
   ]
2299
  },
2300
  {
2301
   "cell_type": "code",
2302
   "execution_count": 42,
2303
   "metadata": {
2304
    "colab": {
2305
     "base_uri": "https://localhost:8080/",
2306
     "height": 430
2307
    },
2308
    "id": "Q3w_QRMIX3SQ",
2309
    "outputId": "f2534bd7-effa-4ea8-9d9b-7330b6c52a75"
2310
   },
2311
   "outputs": [
2312
    {
2313
     "data": {
2314
      "text/html": [
2315
       "<table class=\"simpletable\">\n",
2316
       "<caption>SARIMAX Results</caption>\n",
2317
       "<tr>\n",
2318
       "  <th>Dep. Variable:</th>         <td>Sales</td>      <th>  No. Observations:  </th>    <td>105</td>  \n",
2319
       "</tr>\n",
2320
       "<tr>\n",
2321
       "  <th>Model:</th>            <td>ARIMA(1, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-952.814</td>\n",
2322
       "</tr>\n",
2323
       "<tr>\n",
2324
       "  <th>Date:</th>            <td>Sat, 17 Jun 2023</td> <th>  AIC                </th> <td>1911.627</td>\n",
2325
       "</tr>\n",
2326
       "<tr>\n",
2327
       "  <th>Time:</th>                <td>18:02:26</td>     <th>  BIC                </th> <td>1919.560</td>\n",
2328
       "</tr>\n",
2329
       "<tr>\n",
2330
       "  <th>Sample:</th>             <td>01-01-1964</td>    <th>  HQIC               </th> <td>1914.841</td>\n",
2331
       "</tr>\n",
2332
       "<tr>\n",
2333
       "  <th></th>                   <td>- 09-01-1972</td>   <th>                     </th>     <td> </td>   \n",
2334
       "</tr>\n",
2335
       "<tr>\n",
2336
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
2337
       "</tr>\n",
2338
       "</table>\n",
2339
       "<table class=\"simpletable\">\n",
2340
       "<tr>\n",
2341
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
2342
       "</tr>\n",
2343
       "<tr>\n",
2344
       "  <th>ar.L1</th>  <td>    0.4545</td> <td>    0.114</td> <td>    4.001</td> <td> 0.000</td> <td>    0.232</td> <td>    0.677</td>\n",
2345
       "</tr>\n",
2346
       "<tr>\n",
2347
       "  <th>ma.L1</th>  <td>   -0.9667</td> <td>    0.056</td> <td>  -17.329</td> <td> 0.000</td> <td>   -1.076</td> <td>   -0.857</td>\n",
2348
       "</tr>\n",
2349
       "<tr>\n",
2350
       "  <th>sigma2</th> <td> 5.226e+06</td> <td> 6.17e+05</td> <td>    8.472</td> <td> 0.000</td> <td> 4.02e+06</td> <td> 6.44e+06</td>\n",
2351
       "</tr>\n",
2352
       "</table>\n",
2353
       "<table class=\"simpletable\">\n",
2354
       "<tr>\n",
2355
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.91</td> <th>  Jarque-Bera (JB):  </th> <td>2.59</td>\n",
2356
       "</tr>\n",
2357
       "<tr>\n",
2358
       "  <th>Prob(Q):</th>                <td>0.34</td> <th>  Prob(JB):          </th> <td>0.27</td>\n",
2359
       "</tr>\n",
2360
       "<tr>\n",
2361
       "  <th>Heteroskedasticity (H):</th> <td>3.40</td> <th>  Skew:              </th> <td>0.05</td>\n",
2362
       "</tr>\n",
2363
       "<tr>\n",
2364
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th> <td>3.77</td>\n",
2365
       "</tr>\n",
2366
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
2367
      ],
2368
      "text/plain": [
2369
       "<class 'statsmodels.iolib.summary.Summary'>\n",
2370
       "\"\"\"\n",
2371
       "                               SARIMAX Results                                \n",
2372
       "==============================================================================\n",
2373
       "Dep. Variable:                  Sales   No. Observations:                  105\n",
2374
       "Model:                 ARIMA(1, 1, 1)   Log Likelihood                -952.814\n",
2375
       "Date:                Sat, 17 Jun 2023   AIC                           1911.627\n",
2376
       "Time:                        18:02:26   BIC                           1919.560\n",
2377
       "Sample:                    01-01-1964   HQIC                          1914.841\n",
2378
       "                         - 09-01-1972                                         \n",
2379
       "Covariance Type:                  opg                                         \n",
2380
       "==============================================================================\n",
2381
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
2382
       "------------------------------------------------------------------------------\n",
2383
       "ar.L1          0.4545      0.114      4.001      0.000       0.232       0.677\n",
2384
       "ma.L1         -0.9667      0.056    -17.329      0.000      -1.076      -0.857\n",
2385
       "sigma2      5.226e+06   6.17e+05      8.472      0.000    4.02e+06    6.44e+06\n",
2386
       "===================================================================================\n",
2387
       "Ljung-Box (L1) (Q):                   0.91   Jarque-Bera (JB):                 2.59\n",
2388
       "Prob(Q):                              0.34   Prob(JB):                         0.27\n",
2389
       "Heteroskedasticity (H):               3.40   Skew:                             0.05\n",
2390
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         3.77\n",
2391
       "===================================================================================\n",
2392
       "\n",
2393
       "Warnings:\n",
2394
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
2395
       "\"\"\""
2396
      ]
2397
     },
2398
     "execution_count": 42,
2399
     "metadata": {},
2400
     "output_type": "execute_result"
2401
    }
2402
   ],
2403
   "source": [
2404
    "model_fit.summary()"
2405
   ]
2406
  },
2407
  {
2408
   "cell_type": "code",
2409
   "execution_count": 43,
2410
   "metadata": {
2411
    "colab": {
2412
     "base_uri": "https://localhost:8080/",
2413
     "height": 713
2414
    },
2415
    "id": "ieQj0a_gX3SQ",
2416
    "outputId": "13cc808f-978d-48fc-f89c-873cd0ecea23"
2417
   },
2418
   "outputs": [
2419
    {
2420
     "data": {
2421
      "text/plain": [
2422
       "<Axes: xlabel='Month'>"
2423
      ]
2424
     },
2425
     "execution_count": 43,
2426
     "metadata": {},
2427
     "output_type": "execute_result"
2428
    },
2429
    {
2430
     "data": {
2431
      "image/png": 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\n",
2432
      "text/plain": [
2433
       "<Figure size 1200x800 with 1 Axes>"
2434
      ]
2435
     },
2436
     "metadata": {},
2437
     "output_type": "display_data"
2438
    }
2439
   ],
2440
   "source": [
2441
    "df['forecast']=model_fit.predict(start=90,end=103,dynamic=True)\n",
2442
    "df[['Sales','forecast']].plot(figsize=(12,8))"
2443
   ]
2444
  },
2445
  {
2446
   "cell_type": "code",
2447
   "execution_count": null,
2448
   "metadata": {
2449
    "id": "-IkGgYJNX3SR"
2450
   },
2451
   "outputs": [],
2452
   "source": [
2453
    "## note: when u have seasonal data, use SARIMAX over there.."
2454
   ]
2455
  },
2456
  {
2457
   "cell_type": "code",
2458
   "execution_count": 44,
2459
   "metadata": {
2460
    "id": "RbPGPURIX3SR"
2461
   },
2462
   "outputs": [],
2463
   "source": [
2464
    "import statsmodels.api as sm"
2465
   ]
2466
  },
2467
  {
2468
   "cell_type": "code",
2469
   "execution_count": null,
2470
   "metadata": {
2471
    "id": "MIEKqwKAX3SR"
2472
   },
2473
   "outputs": [],
2474
   "source": [
2475
    "#seasonal_order=(1,1,1,12)\n",
2476
    "#(p,d,q,shift_value)"
2477
   ]
2478
  },
2479
  {
2480
   "cell_type": "code",
2481
   "execution_count": 45,
2482
   "metadata": {
2483
    "colab": {
2484
     "base_uri": "https://localhost:8080/"
2485
    },
2486
    "id": "hk3CIiNrX3SR",
2487
    "outputId": "66cacf8f-89c7-46b6-a187-feb8e8d0093f"
2488
   },
2489
   "outputs": [
2490
    {
2491
     "name": "stderr",
2492
     "output_type": "stream",
2493
     "text": [
2494
      "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
2495
      "  self._init_dates(dates, freq)\n",
2496
      "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
2497
      "  self._init_dates(dates, freq)\n"
2498
     ]
2499
    }
2500
   ],
2501
   "source": [
2502
    "model=sm.tsa.statespace.SARIMAX(df['Sales'],order=(1, 1, 1),seasonal_order=(1,1,1,12))\n",
2503
    "results=model.fit()"
2504
   ]
2505
  },
2506
  {
2507
   "cell_type": "code",
2508
   "execution_count": 46,
2509
   "metadata": {
2510
    "id": "SS-_LFzoX3SR",
2511
    "scrolled": true
2512
   },
2513
   "outputs": [],
2514
   "source": [
2515
    "#predicting from index 90 to 103\n",
2516
    "df['forecast']=results.predict(start=90,end=103,dynamic=True)\n"
2517
   ]
2518
  },
2519
  {
2520
   "cell_type": "code",
2521
   "execution_count": 47,
2522
   "metadata": {
2523
    "colab": {
2524
     "base_uri": "https://localhost:8080/",
2525
     "height": 713
2526
    },
2527
    "id": "TvWeQqmzX3SR",
2528
    "outputId": "b02aa53c-f807-4026-d9d6-c29b6185fa16"
2529
   },
2530
   "outputs": [
2531
    {
2532
     "data": {
2533
      "text/plain": [
2534
       "<Axes: xlabel='Month'>"
2535
      ]
2536
     },
2537
     "execution_count": 47,
2538
     "metadata": {},
2539
     "output_type": "execute_result"
2540
    },
2541
    {
2542
     "data": {
2543
      "image/png": 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\n",
2544
      "text/plain": [
2545
       "<Figure size 1200x800 with 1 Axes>"
2546
      ]
2547
     },
2548
     "metadata": {},
2549
     "output_type": "display_data"
2550
    }
2551
   ],
2552
   "source": [
2553
    "#blue line is original data anf orange is a forecasted data\n",
2554
    "df[['Sales','forecast']].plot(figsize=(12,8))"
2555
   ]
2556
  },
2557
  {
2558
   "cell_type": "code",
2559
   "execution_count": 48,
2560
   "metadata": {
2561
    "id": "kA6zq3rjX3SS"
2562
   },
2563
   "outputs": [],
2564
   "source": [
2565
    "### to see how future predictions/projections looks like"
2566
   ]
2567
  },
2568
  {
2569
   "cell_type": "code",
2570
   "execution_count": 49,
2571
   "metadata": {
2572
    "id": "0km9QA0HX3SS"
2573
   },
2574
   "outputs": [],
2575
   "source": [
2576
    "from pandas.tseries.offsets import DateOffset"
2577
   ]
2578
  },
2579
  {
2580
   "cell_type": "code",
2581
   "execution_count": 50,
2582
   "metadata": {
2583
    "colab": {
2584
     "base_uri": "https://localhost:8080/"
2585
    },
2586
    "id": "-wkEiR90X3SS",
2587
    "outputId": "3181d375-35c4-4269-fc40-13f329cc8813"
2588
   },
2589
   "outputs": [
2590
    {
2591
     "data": {
2592
      "text/plain": [
2593
       "Timestamp('1972-09-01 00:00:00')"
2594
      ]
2595
     },
2596
     "execution_count": 50,
2597
     "metadata": {},
2598
     "output_type": "execute_result"
2599
    }
2600
   ],
2601
   "source": [
2602
    "df.index[-1]"
2603
   ]
2604
  },
2605
  {
2606
   "cell_type": "code",
2607
   "execution_count": 51,
2608
   "metadata": {
2609
    "id": "61P4AT2jX3SS"
2610
   },
2611
   "outputs": [],
2612
   "source": [
2613
    "future_dates=[df.index[-1]+ DateOffset(months=x)for x in range(0,24)]"
2614
   ]
2615
  },
2616
  {
2617
   "cell_type": "code",
2618
   "execution_count": 52,
2619
   "metadata": {
2620
    "colab": {
2621
     "base_uri": "https://localhost:8080/"
2622
    },
2623
    "id": "gsZ0v-CsX3SS",
2624
    "outputId": "c682f0fd-4284-47ea-8c9d-baba7c4630da"
2625
   },
2626
   "outputs": [
2627
    {
2628
     "data": {
2629
      "text/plain": [
2630
       "[Timestamp('1972-09-01 00:00:00'),\n",
2631
       " Timestamp('1972-10-01 00:00:00'),\n",
2632
       " Timestamp('1972-11-01 00:00:00'),\n",
2633
       " Timestamp('1972-12-01 00:00:00'),\n",
2634
       " Timestamp('1973-01-01 00:00:00'),\n",
2635
       " Timestamp('1973-02-01 00:00:00'),\n",
2636
       " Timestamp('1973-03-01 00:00:00'),\n",
2637
       " Timestamp('1973-04-01 00:00:00'),\n",
2638
       " Timestamp('1973-05-01 00:00:00'),\n",
2639
       " Timestamp('1973-06-01 00:00:00'),\n",
2640
       " Timestamp('1973-07-01 00:00:00'),\n",
2641
       " Timestamp('1973-08-01 00:00:00'),\n",
2642
       " Timestamp('1973-09-01 00:00:00'),\n",
2643
       " Timestamp('1973-10-01 00:00:00'),\n",
2644
       " Timestamp('1973-11-01 00:00:00'),\n",
2645
       " Timestamp('1973-12-01 00:00:00'),\n",
2646
       " Timestamp('1974-01-01 00:00:00'),\n",
2647
       " Timestamp('1974-02-01 00:00:00'),\n",
2648
       " Timestamp('1974-03-01 00:00:00'),\n",
2649
       " Timestamp('1974-04-01 00:00:00'),\n",
2650
       " Timestamp('1974-05-01 00:00:00'),\n",
2651
       " Timestamp('1974-06-01 00:00:00'),\n",
2652
       " Timestamp('1974-07-01 00:00:00'),\n",
2653
       " Timestamp('1974-08-01 00:00:00')]"
2654
      ]
2655
     },
2656
     "execution_count": 52,
2657
     "metadata": {},
2658
     "output_type": "execute_result"
2659
    }
2660
   ],
2661
   "source": [
2662
    "future_dates"
2663
   ]
2664
  },
2665
  {
2666
   "cell_type": "code",
2667
   "execution_count": 66,
2668
   "metadata": {
2669
    "id": "xWeZVRo2X3SS"
2670
   },
2671
   "outputs": [],
2672
   "source": [
2673
    "from pandas.tseries.offsets import DateOffset\n",
2674
    "\n",
2675
    "#creating additional dataset for 24 months,\n",
2676
    "future_dates=[df.index[-1]+ DateOffset(months=x)for x in range(0,24)]"
2677
   ]
2678
  },
2679
  {
2680
   "cell_type": "code",
2681
   "execution_count": 67,
2682
   "metadata": {
2683
    "colab": {
2684
     "base_uri": "https://localhost:8080/"
2685
    },
2686
    "id": "qLuyIQhRyWSf",
2687
    "outputId": "3c23627e-b677-4074-c097-9cbd3ed0f383"
2688
   },
2689
   "outputs": [
2690
    {
2691
     "data": {
2692
      "text/plain": [
2693
       "[Timestamp('1972-09-01 00:00:00'),\n",
2694
       " Timestamp('1972-10-01 00:00:00'),\n",
2695
       " Timestamp('1972-11-01 00:00:00'),\n",
2696
       " Timestamp('1972-12-01 00:00:00'),\n",
2697
       " Timestamp('1973-01-01 00:00:00'),\n",
2698
       " Timestamp('1973-02-01 00:00:00'),\n",
2699
       " Timestamp('1973-03-01 00:00:00'),\n",
2700
       " Timestamp('1973-04-01 00:00:00'),\n",
2701
       " Timestamp('1973-05-01 00:00:00'),\n",
2702
       " Timestamp('1973-06-01 00:00:00'),\n",
2703
       " Timestamp('1973-07-01 00:00:00'),\n",
2704
       " Timestamp('1973-08-01 00:00:00'),\n",
2705
       " Timestamp('1973-09-01 00:00:00'),\n",
2706
       " Timestamp('1973-10-01 00:00:00'),\n",
2707
       " Timestamp('1973-11-01 00:00:00'),\n",
2708
       " Timestamp('1973-12-01 00:00:00'),\n",
2709
       " Timestamp('1974-01-01 00:00:00'),\n",
2710
       " Timestamp('1974-02-01 00:00:00'),\n",
2711
       " Timestamp('1974-03-01 00:00:00'),\n",
2712
       " Timestamp('1974-04-01 00:00:00'),\n",
2713
       " Timestamp('1974-05-01 00:00:00'),\n",
2714
       " Timestamp('1974-06-01 00:00:00'),\n",
2715
       " Timestamp('1974-07-01 00:00:00'),\n",
2716
       " Timestamp('1974-08-01 00:00:00')]"
2717
      ]
2718
     },
2719
     "execution_count": 67,
2720
     "metadata": {},
2721
     "output_type": "execute_result"
2722
    }
2723
   ],
2724
   "source": [
2725
    "future_dates"
2726
   ]
2727
  },
2728
  {
2729
   "cell_type": "code",
2730
   "execution_count": 68,
2731
   "metadata": {
2732
    "id": "dIcBcWp4X3ST"
2733
   },
2734
   "outputs": [],
2735
   "source": [
2736
    "future_dataset_df=pd.DataFrame(index=future_dates[1:],columns=df.columns)"
2737
   ]
2738
  },
2739
  {
2740
   "cell_type": "code",
2741
   "execution_count": 69,
2742
   "metadata": {
2743
    "colab": {
2744
     "base_uri": "https://localhost:8080/",
2745
     "height": 206
2746
    },
2747
    "id": "u0HFtl1pX3ST",
2748
    "outputId": "ad15ed3f-cdc7-4e22-97d0-7ada0c064347"
2749
   },
2750
   "outputs": [
2751
    {
2752
     "data": {
2753
      "text/html": [
2754
       "\n",
2755
       "  <div id=\"df-d60fe673-6d48-4faa-a2e0-a69a52f0c8e5\">\n",
2756
       "    <div class=\"colab-df-container\">\n",
2757
       "      <div>\n",
2758
       "<style scoped>\n",
2759
       "    .dataframe tbody tr th:only-of-type {\n",
2760
       "        vertical-align: middle;\n",
2761
       "    }\n",
2762
       "\n",
2763
       "    .dataframe tbody tr th {\n",
2764
       "        vertical-align: top;\n",
2765
       "    }\n",
2766
       "\n",
2767
       "    .dataframe thead th {\n",
2768
       "        text-align: right;\n",
2769
       "    }\n",
2770
       "</style>\n",
2771
       "<table border=\"1\" class=\"dataframe\">\n",
2772
       "  <thead>\n",
2773
       "    <tr style=\"text-align: right;\">\n",
2774
       "      <th></th>\n",
2775
       "      <th>Sales</th>\n",
2776
       "      <th>Sales First Difference</th>\n",
2777
       "      <th>Seasonal First Difference</th>\n",
2778
       "      <th>forecast</th>\n",
2779
       "    </tr>\n",
2780
       "  </thead>\n",
2781
       "  <tbody>\n",
2782
       "    <tr>\n",
2783
       "      <th>1972-10-01</th>\n",
2784
       "      <td>NaN</td>\n",
2785
       "      <td>NaN</td>\n",
2786
       "      <td>NaN</td>\n",
2787
       "      <td>NaN</td>\n",
2788
       "    </tr>\n",
2789
       "    <tr>\n",
2790
       "      <th>1972-11-01</th>\n",
2791
       "      <td>NaN</td>\n",
2792
       "      <td>NaN</td>\n",
2793
       "      <td>NaN</td>\n",
2794
       "      <td>NaN</td>\n",
2795
       "    </tr>\n",
2796
       "    <tr>\n",
2797
       "      <th>1972-12-01</th>\n",
2798
       "      <td>NaN</td>\n",
2799
       "      <td>NaN</td>\n",
2800
       "      <td>NaN</td>\n",
2801
       "      <td>NaN</td>\n",
2802
       "    </tr>\n",
2803
       "    <tr>\n",
2804
       "      <th>1973-01-01</th>\n",
2805
       "      <td>NaN</td>\n",
2806
       "      <td>NaN</td>\n",
2807
       "      <td>NaN</td>\n",
2808
       "      <td>NaN</td>\n",
2809
       "    </tr>\n",
2810
       "    <tr>\n",
2811
       "      <th>1973-02-01</th>\n",
2812
       "      <td>NaN</td>\n",
2813
       "      <td>NaN</td>\n",
2814
       "      <td>NaN</td>\n",
2815
       "      <td>NaN</td>\n",
2816
       "    </tr>\n",
2817
       "  </tbody>\n",
2818
       "</table>\n",
2819
       "</div>\n",
2820
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d60fe673-6d48-4faa-a2e0-a69a52f0c8e5')\"\n",
2821
       "              title=\"Convert this dataframe to an interactive table.\"\n",
2822
       "              style=\"display:none;\">\n",
2823
       "        \n",
2824
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
2825
       "       width=\"24px\">\n",
2826
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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       "    <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",
2828
       "  </svg>\n",
2829
       "      </button>\n",
2830
       "      \n",
2831
       "  <style>\n",
2832
       "    .colab-df-container {\n",
2833
       "      display:flex;\n",
2834
       "      flex-wrap:wrap;\n",
2835
       "      gap: 12px;\n",
2836
       "    }\n",
2837
       "\n",
2838
       "    .colab-df-convert {\n",
2839
       "      background-color: #E8F0FE;\n",
2840
       "      border: none;\n",
2841
       "      border-radius: 50%;\n",
2842
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2843
       "      display: none;\n",
2844
       "      fill: #1967D2;\n",
2845
       "      height: 32px;\n",
2846
       "      padding: 0 0 0 0;\n",
2847
       "      width: 32px;\n",
2848
       "    }\n",
2849
       "\n",
2850
       "    .colab-df-convert:hover {\n",
2851
       "      background-color: #E2EBFA;\n",
2852
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
2853
       "      fill: #174EA6;\n",
2854
       "    }\n",
2855
       "\n",
2856
       "    [theme=dark] .colab-df-convert {\n",
2857
       "      background-color: #3B4455;\n",
2858
       "      fill: #D2E3FC;\n",
2859
       "    }\n",
2860
       "\n",
2861
       "    [theme=dark] .colab-df-convert:hover {\n",
2862
       "      background-color: #434B5C;\n",
2863
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
2864
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
2865
       "      fill: #FFFFFF;\n",
2866
       "    }\n",
2867
       "  </style>\n",
2868
       "\n",
2869
       "      <script>\n",
2870
       "        const buttonEl =\n",
2871
       "          document.querySelector('#df-d60fe673-6d48-4faa-a2e0-a69a52f0c8e5 button.colab-df-convert');\n",
2872
       "        buttonEl.style.display =\n",
2873
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
2874
       "\n",
2875
       "        async function convertToInteractive(key) {\n",
2876
       "          const element = document.querySelector('#df-d60fe673-6d48-4faa-a2e0-a69a52f0c8e5');\n",
2877
       "          const dataTable =\n",
2878
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
2879
       "                                                     [key], {});\n",
2880
       "          if (!dataTable) return;\n",
2881
       "\n",
2882
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
2883
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
2884
       "            + ' to learn more about interactive tables.';\n",
2885
       "          element.innerHTML = '';\n",
2886
       "          dataTable['output_type'] = 'display_data';\n",
2887
       "          await google.colab.output.renderOutput(dataTable, element);\n",
2888
       "          const docLink = document.createElement('div');\n",
2889
       "          docLink.innerHTML = docLinkHtml;\n",
2890
       "          element.appendChild(docLink);\n",
2891
       "        }\n",
2892
       "      </script>\n",
2893
       "    </div>\n",
2894
       "  </div>\n",
2895
       "  "
2896
      ],
2897
      "text/plain": [
2898
       "           Sales Sales First Difference Seasonal First Difference forecast\n",
2899
       "1972-10-01   NaN                    NaN                       NaN      NaN\n",
2900
       "1972-11-01   NaN                    NaN                       NaN      NaN\n",
2901
       "1972-12-01   NaN                    NaN                       NaN      NaN\n",
2902
       "1973-01-01   NaN                    NaN                       NaN      NaN\n",
2903
       "1973-02-01   NaN                    NaN                       NaN      NaN"
2904
      ]
2905
     },
2906
     "execution_count": 69,
2907
     "metadata": {},
2908
     "output_type": "execute_result"
2909
    }
2910
   ],
2911
   "source": [
2912
    "future_dataset_df.head()"
2913
   ]
2914
  },
2915
  {
2916
   "cell_type": "code",
2917
   "execution_count": 71,
2918
   "metadata": {
2919
    "id": "54NWyN37X3ST"
2920
   },
2921
   "outputs": [],
2922
   "source": [
2923
    "future_df=pd.concat([df,future_dataset_df])"
2924
   ]
2925
  },
2926
  {
2927
   "cell_type": "code",
2928
   "execution_count": null,
2929
   "metadata": {
2930
    "id": "b8kpElBjX3ST"
2931
   },
2932
   "outputs": [],
2933
   "source": [
2934
    "#then we can see future predictions"
2935
   ]
2936
  },
2937
  {
2938
   "cell_type": "code",
2939
   "execution_count": 72,
2940
   "metadata": {
2941
    "colab": {
2942
     "base_uri": "https://localhost:8080/",
2943
     "height": 693
2944
    },
2945
    "id": "C0blbfakX3ST",
2946
    "outputId": "b53d96a0-3f7b-4ea7-dd88-c2b3618cdaff"
2947
   },
2948
   "outputs": [
2949
    {
2950
     "data": {
2951
      "text/plain": [
2952
       "<Axes: >"
2953
      ]
2954
     },
2955
     "execution_count": 72,
2956
     "metadata": {},
2957
     "output_type": "execute_result"
2958
    },
2959
    {
2960
     "data": {
2961
      "image/png": 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\n",
2962
      "text/plain": [
2963
       "<Figure size 1200x800 with 1 Axes>"
2964
      ]
2965
     },
2966
     "metadata": {},
2967
     "output_type": "display_data"
2968
    }
2969
   ],
2970
   "source": [
2971
    "future_df['forecast'] = results.predict(start = 104, end = 120, dynamic= True)\n",
2972
    "future_df[['Sales', 'forecast']].plot(figsize=(12, 8))"
2973
   ]
2974
  }
2975
 ],
2976
 "metadata": {
2977
  "colab": {
2978
   "provenance": []
2979
  },
2980
  "kernelspec": {
2981
   "display_name": "Python 3 (ipykernel)",
2982
   "language": "python",
2983
   "name": "python3"
2984
  },
2985
  "language_info": {
2986
   "codemirror_mode": {
2987
    "name": "ipython",
2988
    "version": 3
2989
   },
2990
   "file_extension": ".py",
2991
   "mimetype": "text/x-python",
2992
   "name": "python",
2993
   "nbconvert_exporter": "python",
2994
   "pygments_lexer": "ipython3",
2995
   "version": "3.9.13"
2996
  }
2997
 },
2998
 "nbformat": 4,
2999
 "nbformat_minor": 1
3000
}
3001