Anomaly_Detection_using_Isolation_Forest_Feature_Engineering.ipynb

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      "source": [
        "![link text](https://www.anblicks.com/wp-content/uploads/2020/11/contextual-outlier.png)\r\n",
        "\r\n",
        "Image Source: anblick.com"
      ]
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        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "from datetime import datetime\n",
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "\n",
        "from sklearn.ensemble import IsolationForest\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (10, 8)\n",
        "mpl.rcParams['axes.grid'] = False\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJaoc_EcR1pB"
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      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/numenta/NAB/master/data/realKnownCause/nyc_taxi.csv')"
      ],
      "execution_count": null,
      "outputs": []
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              "                 timestamp  value\n",
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        "fig = px.line(df.reset_index(), x='timestamp', y='value', title='NYC Taxi Demand')\r\n",
        "\r\n",
        "fig.update_xaxes(\r\n",
        "    rangeslider_visible=True,\r\n",
        ")\r\n",
        "fig.show()"
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              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XxNpDqe6HuE"
      },
      "source": [
        "df['hour']=df.timestamp.dt.hour"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6M83uVNdDRl"
      },
      "source": [
        "df['weekday']=pd.Categorical(df.timestamp.dt.strftime('%A'), categories=['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday'], ordered=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GCrp8tct1VgM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19119e9e-aa19-4ced-b547-5cfb24faf100"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5160 entries, 0 to 5159\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count  Dtype         \n",
            "---  ------     --------------  -----         \n",
            " 0   timestamp  5160 non-null   datetime64[ns]\n",
            " 1   value      5160 non-null   float64       \n",
            " 2   hour       5160 non-null   int64         \n",
            " 3   weekday    5160 non-null   category      \n",
            "dtypes: category(1), datetime64[ns](1), float64(1), int64(1)\n",
            "memory usage: 126.5 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LeRWC1aJ_CMa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "d97be5fa-056e-4445-b4ca-0fa5324b7243"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>value</th>\n",
              "      <th>hour</th>\n",
              "      <th>weekday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-07-01 00:00:00</td>\n",
              "      <td>9485.5</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-07-01 01:00:00</td>\n",
              "      <td>5433.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-07-01 02:00:00</td>\n",
              "      <td>3346.5</td>\n",
              "      <td>2</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-07-01 03:00:00</td>\n",
              "      <td>2216.5</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-07-01 04:00:00</td>\n",
              "      <td>2189.5</td>\n",
              "      <td>4</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5155</th>\n",
              "      <td>2015-01-31 19:00:00</td>\n",
              "      <td>28288.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5156</th>\n",
              "      <td>2015-01-31 20:00:00</td>\n",
              "      <td>24138.0</td>\n",
              "      <td>20</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5157</th>\n",
              "      <td>2015-01-31 21:00:00</td>\n",
              "      <td>24194.5</td>\n",
              "      <td>21</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5158</th>\n",
              "      <td>2015-01-31 22:00:00</td>\n",
              "      <td>26515.0</td>\n",
              "      <td>22</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5159</th>\n",
              "      <td>2015-01-31 23:00:00</td>\n",
              "      <td>26439.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5160 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour   weekday\n",
              "0    2014-07-01 00:00:00   9485.5     0   Tuesday\n",
              "1    2014-07-01 01:00:00   5433.0     1   Tuesday\n",
              "2    2014-07-01 02:00:00   3346.5     2   Tuesday\n",
              "3    2014-07-01 03:00:00   2216.5     3   Tuesday\n",
              "4    2014-07-01 04:00:00   2189.5     4   Tuesday\n",
              "...                  ...      ...   ...       ...\n",
              "5155 2015-01-31 19:00:00  28288.5    19  Saturday\n",
              "5156 2015-01-31 20:00:00  24138.0    20  Saturday\n",
              "5157 2015-01-31 21:00:00  24194.5    21  Saturday\n",
              "5158 2015-01-31 22:00:00  26515.0    22  Saturday\n",
              "5159 2015-01-31 23:00:00  26439.5    23  Saturday\n",
              "\n",
              "[5160 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4I-m1BsDALW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "c16f9478-aed0-46ea-f2a2-6bb13842ecc3"
      },
      "source": [
        "df[['value','hour']].groupby('hour').mean().plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f06133e7950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iP4nsGOG_B0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "5ea1f270-dbed-467a-8882-cbe99ade5e55"
      },
      "source": [
        "df[['value','weekday']].groupby('weekday').mean().plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f06122ec4d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "tmnDYdouo_n8",
        "outputId": "124de20d-e342-40d6-96ca-311d2ff996b2"
      },
      "source": [
        "df.groupby(['hour', 'weekday']).mean()['value'].unstack().plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0611dcb5d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "QXnrxt5D1CF5",
        "outputId": "b71a6600-a8a7-4c15-e484-da37535b9efb"
      },
      "source": [
        "df.join(df.groupby(['hour', 'weekday'])['value'].mean(), on=['hour','weekday'], rsuffix='_avg')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>value</th>\n",
              "      <th>hour</th>\n",
              "      <th>weekday</th>\n",
              "      <th>value_avg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-07-01 00:00:00</td>\n",
              "      <td>9485.5</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>8483.709677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-07-01 01:00:00</td>\n",
              "      <td>5433.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>5028.193548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-07-01 02:00:00</td>\n",
              "      <td>3346.5</td>\n",
              "      <td>2</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>3052.112903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-07-01 03:00:00</td>\n",
              "      <td>2216.5</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2039.580645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-07-01 04:00:00</td>\n",
              "      <td>2189.5</td>\n",
              "      <td>4</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2031.258065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5155</th>\n",
              "      <td>2015-01-31 19:00:00</td>\n",
              "      <td>28288.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>24501.870968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5156</th>\n",
              "      <td>2015-01-31 20:00:00</td>\n",
              "      <td>24138.0</td>\n",
              "      <td>20</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>22193.758065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5157</th>\n",
              "      <td>2015-01-31 21:00:00</td>\n",
              "      <td>24194.5</td>\n",
              "      <td>21</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>21983.241935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5158</th>\n",
              "      <td>2015-01-31 22:00:00</td>\n",
              "      <td>26515.0</td>\n",
              "      <td>22</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>23949.951613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5159</th>\n",
              "      <td>2015-01-31 23:00:00</td>\n",
              "      <td>26439.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>25192.516129</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5160 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour   weekday     value_avg\n",
              "0    2014-07-01 00:00:00   9485.5     0   Tuesday   8483.709677\n",
              "1    2014-07-01 01:00:00   5433.0     1   Tuesday   5028.193548\n",
              "2    2014-07-01 02:00:00   3346.5     2   Tuesday   3052.112903\n",
              "3    2014-07-01 03:00:00   2216.5     3   Tuesday   2039.580645\n",
              "4    2014-07-01 04:00:00   2189.5     4   Tuesday   2031.258065\n",
              "...                  ...      ...   ...       ...           ...\n",
              "5155 2015-01-31 19:00:00  28288.5    19  Saturday  24501.870968\n",
              "5156 2015-01-31 20:00:00  24138.0    20  Saturday  22193.758065\n",
              "5157 2015-01-31 21:00:00  24194.5    21  Saturday  21983.241935\n",
              "5158 2015-01-31 22:00:00  26515.0    22  Saturday  23949.951613\n",
              "5159 2015-01-31 23:00:00  26439.5    23  Saturday  25192.516129\n",
              "\n",
              "[5160 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jeLoqzjuakQ"
      },
      "source": [
        "df_final=df.join(df.groupby(['hour', 'weekday'])['value'].mean(), on=['hour','weekday'], rsuffix='_avg')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "XziWQUUpuaVy",
        "outputId": "765ded08-f216-4a8e-dbe3-72f381e1ec6d"
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      "source": [
        "df_final"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "      <td>8483.709677</td>\n",
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              "      <th>1</th>\n",
              "      <td>2014-07-01 01:00:00</td>\n",
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              "      <th>2</th>\n",
              "      <td>2014-07-01 02:00:00</td>\n",
              "      <td>3346.5</td>\n",
              "      <td>2</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>3052.112903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-07-01 03:00:00</td>\n",
              "      <td>2216.5</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2039.580645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-07-01 04:00:00</td>\n",
              "      <td>2189.5</td>\n",
              "      <td>4</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2031.258065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5155</th>\n",
              "      <td>2015-01-31 19:00:00</td>\n",
              "      <td>28288.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>24501.870968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5156</th>\n",
              "      <td>2015-01-31 20:00:00</td>\n",
              "      <td>24138.0</td>\n",
              "      <td>20</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>22193.758065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5157</th>\n",
              "      <td>2015-01-31 21:00:00</td>\n",
              "      <td>24194.5</td>\n",
              "      <td>21</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>21983.241935</td>\n",
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              "    <tr>\n",
              "      <th>5158</th>\n",
              "      <td>2015-01-31 22:00:00</td>\n",
              "      <td>26515.0</td>\n",
              "      <td>22</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>23949.951613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5159</th>\n",
              "      <td>2015-01-31 23:00:00</td>\n",
              "      <td>26439.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>25192.516129</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5160 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour   weekday     value_avg\n",
              "0    2014-07-01 00:00:00   9485.5     0   Tuesday   8483.709677\n",
              "1    2014-07-01 01:00:00   5433.0     1   Tuesday   5028.193548\n",
              "2    2014-07-01 02:00:00   3346.5     2   Tuesday   3052.112903\n",
              "3    2014-07-01 03:00:00   2216.5     3   Tuesday   2039.580645\n",
              "4    2014-07-01 04:00:00   2189.5     4   Tuesday   2031.258065\n",
              "...                  ...      ...   ...       ...           ...\n",
              "5155 2015-01-31 19:00:00  28288.5    19  Saturday  24501.870968\n",
              "5156 2015-01-31 20:00:00  24138.0    20  Saturday  22193.758065\n",
              "5157 2015-01-31 21:00:00  24194.5    21  Saturday  21983.241935\n",
              "5158 2015-01-31 22:00:00  26515.0    22  Saturday  23949.951613\n",
              "5159 2015-01-31 23:00:00  26439.5    23  Saturday  25192.516129\n",
              "\n",
              "[5160 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfaGCvsSZdoy"
      },
      "source": [
        "Anomalous Point\n",
        "\n",
        "```\n",
        "NYC Marathon - 2014-11-02\n",
        "Thanksgiving - 2014-11-27\n",
        "Christmas - 2014-12-25\n",
        "New Years - 2015-01-01\n",
        "Snow Blizzard - 2015-01-26 and 2015-01-27```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "mcYsmucB4OEJ",
        "outputId": "b87f860c-c93e-492c-ee08-aaf570cfdb21"
      },
      "source": [
        "df_final.set_index('timestamp').loc['2014-11-02']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "      <th>hour</th>\n",
              "      <th>weekday</th>\n",
              "      <th>value_avg</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>timestamp</th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-11-02 00:00:00</th>\n",
              "      <td>24109.5</td>\n",
              "      <td>0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>23898.733333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 01:00:00</th>\n",
              "      <td>37204.5</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>21593.533333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 02:00:00</th>\n",
              "      <td>12754.5</td>\n",
              "      <td>2</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>17555.266667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 03:00:00</th>\n",
              "      <td>8955.5</td>\n",
              "      <td>3</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>13350.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 04:00:00</th>\n",
              "      <td>5453.5</td>\n",
              "      <td>4</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>8506.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 05:00:00</th>\n",
              "      <td>5174.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3837.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 06:00:00</th>\n",
              "      <td>4920.0</td>\n",
              "      <td>6</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3643.983333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 07:00:00</th>\n",
              "      <td>5592.0</td>\n",
              "      <td>7</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>4711.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 08:00:00</th>\n",
              "      <td>7525.5</td>\n",
              "      <td>8</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>7159.516667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 09:00:00</th>\n",
              "      <td>11326.0</td>\n",
              "      <td>9</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>10815.116667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 10:00:00</th>\n",
              "      <td>15262.0</td>\n",
              "      <td>10</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>14906.283333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 11:00:00</th>\n",
              "      <td>17954.0</td>\n",
              "      <td>11</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>17487.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 12:00:00</th>\n",
              "      <td>19448.0</td>\n",
              "      <td>12</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>18994.566667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 13:00:00</th>\n",
              "      <td>19323.5</td>\n",
              "      <td>13</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>18975.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 14:00:00</th>\n",
              "      <td>19002.0</td>\n",
              "      <td>14</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>19021.383333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 15:00:00</th>\n",
              "      <td>17944.5</td>\n",
              "      <td>15</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>18054.083333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 16:00:00</th>\n",
              "      <td>16807.0</td>\n",
              "      <td>16</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>16750.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 17:00:00</th>\n",
              "      <td>18382.5</td>\n",
              "      <td>17</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>17941.383333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 18:00:00</th>\n",
              "      <td>20970.0</td>\n",
              "      <td>18</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>19310.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 19:00:00</th>\n",
              "      <td>22305.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>18454.133333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 20:00:00</th>\n",
              "      <td>20384.0</td>\n",
              "      <td>20</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>16797.733333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 21:00:00</th>\n",
              "      <td>18081.0</td>\n",
              "      <td>21</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>16036.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 22:00:00</th>\n",
              "      <td>16382.5</td>\n",
              "      <td>22</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>14499.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-02 23:00:00</th>\n",
              "      <td>11591.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>11686.450000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       value  hour weekday     value_avg\n",
              "timestamp                                               \n",
              "2014-11-02 00:00:00  24109.5     0  Sunday  23898.733333\n",
              "2014-11-02 01:00:00  37204.5     1  Sunday  21593.533333\n",
              "2014-11-02 02:00:00  12754.5     2  Sunday  17555.266667\n",
              "2014-11-02 03:00:00   8955.5     3  Sunday  13350.550000\n",
              "2014-11-02 04:00:00   5453.5     4  Sunday   8506.500000\n",
              "2014-11-02 05:00:00   5174.0     5  Sunday   3837.450000\n",
              "2014-11-02 06:00:00   4920.0     6  Sunday   3643.983333\n",
              "2014-11-02 07:00:00   5592.0     7  Sunday   4711.300000\n",
              "2014-11-02 08:00:00   7525.5     8  Sunday   7159.516667\n",
              "2014-11-02 09:00:00  11326.0     9  Sunday  10815.116667\n",
              "2014-11-02 10:00:00  15262.0    10  Sunday  14906.283333\n",
              "2014-11-02 11:00:00  17954.0    11  Sunday  17487.100000\n",
              "2014-11-02 12:00:00  19448.0    12  Sunday  18994.566667\n",
              "2014-11-02 13:00:00  19323.5    13  Sunday  18975.800000\n",
              "2014-11-02 14:00:00  19002.0    14  Sunday  19021.383333\n",
              "2014-11-02 15:00:00  17944.5    15  Sunday  18054.083333\n",
              "2014-11-02 16:00:00  16807.0    16  Sunday  16750.333333\n",
              "2014-11-02 17:00:00  18382.5    17  Sunday  17941.383333\n",
              "2014-11-02 18:00:00  20970.0    18  Sunday  19310.900000\n",
              "2014-11-02 19:00:00  22305.5    19  Sunday  18454.133333\n",
              "2014-11-02 20:00:00  20384.0    20  Sunday  16797.733333\n",
              "2014-11-02 21:00:00  18081.0    21  Sunday  16036.950000\n",
              "2014-11-02 22:00:00  16382.5    22  Sunday  14499.900000\n",
              "2014-11-02 23:00:00  11591.0    23  Sunday  11686.450000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xsFsT0Qz4_i3",
        "outputId": "e53f7476-0538-4b5c-ce20-646ffa66cf84"
      },
      "source": [
        "df_final.set_index('timestamp').loc['2015-01-26':'2015-01-27']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "      <th>hour</th>\n",
              "      <th>weekday</th>\n",
              "      <th>value_avg</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>timestamp</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-01-26 00:00:00</th>\n",
              "      <td>5907.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>8020.283333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 01:00:00</th>\n",
              "      <td>3649.5</td>\n",
              "      <td>1</td>\n",
              "      <td>Monday</td>\n",
              "      <td>4749.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 02:00:00</th>\n",
              "      <td>2310.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Monday</td>\n",
              "      <td>3125.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 03:00:00</th>\n",
              "      <td>1949.5</td>\n",
              "      <td>3</td>\n",
              "      <td>Monday</td>\n",
              "      <td>2195.133333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 04:00:00</th>\n",
              "      <td>2165.5</td>\n",
              "      <td>4</td>\n",
              "      <td>Monday</td>\n",
              "      <td>2373.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 05:00:00</th>\n",
              "      <td>3723.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Monday</td>\n",
              "      <td>3550.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 06:00:00</th>\n",
              "      <td>9146.0</td>\n",
              "      <td>6</td>\n",
              "      <td>Monday</td>\n",
              "      <td>8414.566667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 07:00:00</th>\n",
              "      <td>15835.5</td>\n",
              "      <td>7</td>\n",
              "      <td>Monday</td>\n",
              "      <td>13968.766667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 08:00:00</th>\n",
              "      <td>18804.5</td>\n",
              "      <td>8</td>\n",
              "      <td>Monday</td>\n",
              "      <td>16774.483333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 09:00:00</th>\n",
              "      <td>16626.0</td>\n",
              "      <td>9</td>\n",
              "      <td>Monday</td>\n",
              "      <td>16483.483333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 10:00:00</th>\n",
              "      <td>13845.0</td>\n",
              "      <td>10</td>\n",
              "      <td>Monday</td>\n",
              "      <td>15132.866667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 11:00:00</th>\n",
              "      <td>13834.5</td>\n",
              "      <td>11</td>\n",
              "      <td>Monday</td>\n",
              "      <td>15391.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 12:00:00</th>\n",
              "      <td>14458.0</td>\n",
              "      <td>12</td>\n",
              "      <td>Monday</td>\n",
              "      <td>16150.116667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 13:00:00</th>\n",
              "      <td>14574.5</td>\n",
              "      <td>13</td>\n",
              "      <td>Monday</td>\n",
              "      <td>16220.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 14:00:00</th>\n",
              "      <td>12673.5</td>\n",
              "      <td>14</td>\n",
              "      <td>Monday</td>\n",
              "      <td>17180.616667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 15:00:00</th>\n",
              "      <td>8670.0</td>\n",
              "      <td>15</td>\n",
              "      <td>Monday</td>\n",
              "      <td>16831.283333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 16:00:00</th>\n",
              "      <td>5888.5</td>\n",
              "      <td>16</td>\n",
              "      <td>Monday</td>\n",
              "      <td>14833.983333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 17:00:00</th>\n",
              "      <td>5221.0</td>\n",
              "      <td>17</td>\n",
              "      <td>Monday</td>\n",
              "      <td>17714.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 18:00:00</th>\n",
              "      <td>5703.5</td>\n",
              "      <td>18</td>\n",
              "      <td>Monday</td>\n",
              "      <td>21190.150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 19:00:00</th>\n",
              "      <td>4565.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Monday</td>\n",
              "      <td>21255.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 20:00:00</th>\n",
              "      <td>3630.5</td>\n",
              "      <td>20</td>\n",
              "      <td>Monday</td>\n",
              "      <td>19880.766667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 21:00:00</th>\n",
              "      <td>2907.0</td>\n",
              "      <td>21</td>\n",
              "      <td>Monday</td>\n",
              "      <td>19197.350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 22:00:00</th>\n",
              "      <td>1324.5</td>\n",
              "      <td>22</td>\n",
              "      <td>Monday</td>\n",
              "      <td>17191.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26 23:00:00</th>\n",
              "      <td>243.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Monday</td>\n",
              "      <td>12863.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 00:00:00</th>\n",
              "      <td>94.5</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>8483.709677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 01:00:00</th>\n",
              "      <td>39.5</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>5028.193548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 02:00:00</th>\n",
              "      <td>29.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>3052.112903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 03:00:00</th>\n",
              "      <td>9.5</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2039.580645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 04:00:00</th>\n",
              "      <td>14.5</td>\n",
              "      <td>4</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2031.258065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 05:00:00</th>\n",
              "      <td>29.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>3207.338710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 06:00:00</th>\n",
              "      <td>88.0</td>\n",
              "      <td>6</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>8576.403226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 07:00:00</th>\n",
              "      <td>274.0</td>\n",
              "      <td>7</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>15314.741935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 08:00:00</th>\n",
              "      <td>809.5</td>\n",
              "      <td>8</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>18504.435484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 09:00:00</th>\n",
              "      <td>1937.0</td>\n",
              "      <td>9</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>17805.919355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 10:00:00</th>\n",
              "      <td>3244.5</td>\n",
              "      <td>10</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>16311.080645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 11:00:00</th>\n",
              "      <td>4205.5</td>\n",
              "      <td>11</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>16254.741935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 12:00:00</th>\n",
              "      <td>5040.0</td>\n",
              "      <td>12</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>17051.096774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 13:00:00</th>\n",
              "      <td>5428.5</td>\n",
              "      <td>13</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>16915.225806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 14:00:00</th>\n",
              "      <td>6171.0</td>\n",
              "      <td>14</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>17939.048387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 15:00:00</th>\n",
              "      <td>7203.5</td>\n",
              "      <td>15</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>17028.483871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 16:00:00</th>\n",
              "      <td>8361.0</td>\n",
              "      <td>16</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>13960.806452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 17:00:00</th>\n",
              "      <td>10319.5</td>\n",
              "      <td>17</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>17461.387097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 18:00:00</th>\n",
              "      <td>12614.0</td>\n",
              "      <td>18</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>22233.596774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 19:00:00</th>\n",
              "      <td>11390.0</td>\n",
              "      <td>19</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>23166.032258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 20:00:00</th>\n",
              "      <td>10275.5</td>\n",
              "      <td>20</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>22035.870968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 21:00:00</th>\n",
              "      <td>10819.5</td>\n",
              "      <td>21</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>22310.741935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 22:00:00</th>\n",
              "      <td>9840.0</td>\n",
              "      <td>22</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>20481.516129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27 23:00:00</th>\n",
              "      <td>7792.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>16217.370968</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       value  hour  weekday     value_avg\n",
              "timestamp                                                \n",
              "2015-01-26 00:00:00   5907.0     0   Monday   8020.283333\n",
              "2015-01-26 01:00:00   3649.5     1   Monday   4749.750000\n",
              "2015-01-26 02:00:00   2310.0     2   Monday   3125.800000\n",
              "2015-01-26 03:00:00   1949.5     3   Monday   2195.133333\n",
              "2015-01-26 04:00:00   2165.5     4   Monday   2373.166667\n",
              "2015-01-26 05:00:00   3723.0     5   Monday   3550.800000\n",
              "2015-01-26 06:00:00   9146.0     6   Monday   8414.566667\n",
              "2015-01-26 07:00:00  15835.5     7   Monday  13968.766667\n",
              "2015-01-26 08:00:00  18804.5     8   Monday  16774.483333\n",
              "2015-01-26 09:00:00  16626.0     9   Monday  16483.483333\n",
              "2015-01-26 10:00:00  13845.0    10   Monday  15132.866667\n",
              "2015-01-26 11:00:00  13834.5    11   Monday  15391.800000\n",
              "2015-01-26 12:00:00  14458.0    12   Monday  16150.116667\n",
              "2015-01-26 13:00:00  14574.5    13   Monday  16220.200000\n",
              "2015-01-26 14:00:00  12673.5    14   Monday  17180.616667\n",
              "2015-01-26 15:00:00   8670.0    15   Monday  16831.283333\n",
              "2015-01-26 16:00:00   5888.5    16   Monday  14833.983333\n",
              "2015-01-26 17:00:00   5221.0    17   Monday  17714.100000\n",
              "2015-01-26 18:00:00   5703.5    18   Monday  21190.150000\n",
              "2015-01-26 19:00:00   4565.5    19   Monday  21255.900000\n",
              "2015-01-26 20:00:00   3630.5    20   Monday  19880.766667\n",
              "2015-01-26 21:00:00   2907.0    21   Monday  19197.350000\n",
              "2015-01-26 22:00:00   1324.5    22   Monday  17191.700000\n",
              "2015-01-26 23:00:00    243.0    23   Monday  12863.000000\n",
              "2015-01-27 00:00:00     94.5     0  Tuesday   8483.709677\n",
              "2015-01-27 01:00:00     39.5     1  Tuesday   5028.193548\n",
              "2015-01-27 02:00:00     29.0     2  Tuesday   3052.112903\n",
              "2015-01-27 03:00:00      9.5     3  Tuesday   2039.580645\n",
              "2015-01-27 04:00:00     14.5     4  Tuesday   2031.258065\n",
              "2015-01-27 05:00:00     29.0     5  Tuesday   3207.338710\n",
              "2015-01-27 06:00:00     88.0     6  Tuesday   8576.403226\n",
              "2015-01-27 07:00:00    274.0     7  Tuesday  15314.741935\n",
              "2015-01-27 08:00:00    809.5     8  Tuesday  18504.435484\n",
              "2015-01-27 09:00:00   1937.0     9  Tuesday  17805.919355\n",
              "2015-01-27 10:00:00   3244.5    10  Tuesday  16311.080645\n",
              "2015-01-27 11:00:00   4205.5    11  Tuesday  16254.741935\n",
              "2015-01-27 12:00:00   5040.0    12  Tuesday  17051.096774\n",
              "2015-01-27 13:00:00   5428.5    13  Tuesday  16915.225806\n",
              "2015-01-27 14:00:00   6171.0    14  Tuesday  17939.048387\n",
              "2015-01-27 15:00:00   7203.5    15  Tuesday  17028.483871\n",
              "2015-01-27 16:00:00   8361.0    16  Tuesday  13960.806452\n",
              "2015-01-27 17:00:00  10319.5    17  Tuesday  17461.387097\n",
              "2015-01-27 18:00:00  12614.0    18  Tuesday  22233.596774\n",
              "2015-01-27 19:00:00  11390.0    19  Tuesday  23166.032258\n",
              "2015-01-27 20:00:00  10275.5    20  Tuesday  22035.870968\n",
              "2015-01-27 21:00:00  10819.5    21  Tuesday  22310.741935\n",
              "2015-01-27 22:00:00   9840.0    22  Tuesday  20481.516129\n",
              "2015-01-27 23:00:00   7792.0    23  Tuesday  16217.370968"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDa85OpY6iYH"
      },
      "source": [
        "df_final['day']=df.timestamp.dt.weekday"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "2m54fFk-67dk",
        "outputId": "44a3ab57-1302-43a4-ff37-34b3b939f703"
      },
      "source": [
        "df_final"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>timestamp</th>\n",
              "      <th>value</th>\n",
              "      <th>hour</th>\n",
              "      <th>weekday</th>\n",
              "      <th>value_avg</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-07-01 00:00:00</td>\n",
              "      <td>9485.5</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>8483.709677</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-07-01 01:00:00</td>\n",
              "      <td>5433.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>5028.193548</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-07-01 02:00:00</td>\n",
              "      <td>3346.5</td>\n",
              "      <td>2</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>3052.112903</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-07-01 03:00:00</td>\n",
              "      <td>2216.5</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2039.580645</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-07-01 04:00:00</td>\n",
              "      <td>2189.5</td>\n",
              "      <td>4</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2031.258065</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5155</th>\n",
              "      <td>2015-01-31 19:00:00</td>\n",
              "      <td>28288.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>24501.870968</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5156</th>\n",
              "      <td>2015-01-31 20:00:00</td>\n",
              "      <td>24138.0</td>\n",
              "      <td>20</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>22193.758065</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5157</th>\n",
              "      <td>2015-01-31 21:00:00</td>\n",
              "      <td>24194.5</td>\n",
              "      <td>21</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>21983.241935</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5158</th>\n",
              "      <td>2015-01-31 22:00:00</td>\n",
              "      <td>26515.0</td>\n",
              "      <td>22</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>23949.951613</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5159</th>\n",
              "      <td>2015-01-31 23:00:00</td>\n",
              "      <td>26439.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>25192.516129</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5160 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour   weekday     value_avg  day\n",
              "0    2014-07-01 00:00:00   9485.5     0   Tuesday   8483.709677    1\n",
              "1    2014-07-01 01:00:00   5433.0     1   Tuesday   5028.193548    1\n",
              "2    2014-07-01 02:00:00   3346.5     2   Tuesday   3052.112903    1\n",
              "3    2014-07-01 03:00:00   2216.5     3   Tuesday   2039.580645    1\n",
              "4    2014-07-01 04:00:00   2189.5     4   Tuesday   2031.258065    1\n",
              "...                  ...      ...   ...       ...           ...  ...\n",
              "5155 2015-01-31 19:00:00  28288.5    19  Saturday  24501.870968    5\n",
              "5156 2015-01-31 20:00:00  24138.0    20  Saturday  22193.758065    5\n",
              "5157 2015-01-31 21:00:00  24194.5    21  Saturday  21983.241935    5\n",
              "5158 2015-01-31 22:00:00  26515.0    22  Saturday  23949.951613    5\n",
              "5159 2015-01-31 23:00:00  26439.5    23  Saturday  25192.516129    5\n",
              "\n",
              "[5160 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AwLWcIxp7Gpa"
      },
      "source": [
        "data = df_final[['value', 'hour', 'day']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwvrxTpCHZF5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f188e8f-886f-43f7-830c-9a7a876af057"
      },
      "source": [
        "model =  IsolationForest(contamination=0.005, max_features=3, max_samples=0.8, n_estimators=200)\n",
        "model.fit(data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IsolationForest(behaviour='deprecated', bootstrap=False, contamination=0.005,\n",
              "                max_features=3, max_samples=0.8, n_estimators=200, n_jobs=None,\n",
              "                random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LsrVVLVjIrmW"
      },
      "source": [
        "df_final['outliers']=pd.Series(model.predict(data)).apply(lambda x: 'yes' if (x == -1) else 'no' )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynUws7-BMnf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 846
        },
        "outputId": "a79849e2-b45b-4a6a-be06-71356b34f309"
      },
      "source": [
        "df_final.query('outliers==\"yes\"')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>value</th>\n",
              "      <th>hour</th>\n",
              "      <th>weekday</th>\n",
              "      <th>value_avg</th>\n",
              "      <th>day</th>\n",
              "      <th>outliers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1488</th>\n",
              "      <td>2014-09-01 00:00:00</td>\n",
              "      <td>13763.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>8020.283333</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1631</th>\n",
              "      <td>2014-09-06 23:00:00</td>\n",
              "      <td>29418.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>25192.516129</td>\n",
              "      <td>5</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2640</th>\n",
              "      <td>2014-10-19 00:00:00</td>\n",
              "      <td>27146.5</td>\n",
              "      <td>0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>23898.733333</td>\n",
              "      <td>6</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2977</th>\n",
              "      <td>2014-11-02 01:00:00</td>\n",
              "      <td>37204.5</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>21593.533333</td>\n",
              "      <td>6</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4271</th>\n",
              "      <td>2014-12-25 23:00:00</td>\n",
              "      <td>7977.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>20732.241935</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4413</th>\n",
              "      <td>2014-12-31 21:00:00</td>\n",
              "      <td>27559.5</td>\n",
              "      <td>21</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>23345.806452</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4416</th>\n",
              "      <td>2015-01-01 00:00:00</td>\n",
              "      <td>25850.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>12698.500000</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4417</th>\n",
              "      <td>2015-01-01 01:00:00</td>\n",
              "      <td>29292.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>8132.564516</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4418</th>\n",
              "      <td>2015-01-01 02:00:00</td>\n",
              "      <td>25753.5</td>\n",
              "      <td>2</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>5350.209677</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4419</th>\n",
              "      <td>2015-01-01 03:00:00</td>\n",
              "      <td>22067.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>3683.177419</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4511</th>\n",
              "      <td>2015-01-04 23:00:00</td>\n",
              "      <td>8402.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>11686.450000</td>\n",
              "      <td>6</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4871</th>\n",
              "      <td>2015-01-19 23:00:00</td>\n",
              "      <td>9294.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Monday</td>\n",
              "      <td>12863.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5015</th>\n",
              "      <td>2015-01-25 23:00:00</td>\n",
              "      <td>9077.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>11686.450000</td>\n",
              "      <td>6</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5033</th>\n",
              "      <td>2015-01-26 17:00:00</td>\n",
              "      <td>5221.0</td>\n",
              "      <td>17</td>\n",
              "      <td>Monday</td>\n",
              "      <td>17714.100000</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5034</th>\n",
              "      <td>2015-01-26 18:00:00</td>\n",
              "      <td>5703.5</td>\n",
              "      <td>18</td>\n",
              "      <td>Monday</td>\n",
              "      <td>21190.150000</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5035</th>\n",
              "      <td>2015-01-26 19:00:00</td>\n",
              "      <td>4565.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Monday</td>\n",
              "      <td>21255.900000</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5036</th>\n",
              "      <td>2015-01-26 20:00:00</td>\n",
              "      <td>3630.5</td>\n",
              "      <td>20</td>\n",
              "      <td>Monday</td>\n",
              "      <td>19880.766667</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5037</th>\n",
              "      <td>2015-01-26 21:00:00</td>\n",
              "      <td>2907.0</td>\n",
              "      <td>21</td>\n",
              "      <td>Monday</td>\n",
              "      <td>19197.350000</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5038</th>\n",
              "      <td>2015-01-26 22:00:00</td>\n",
              "      <td>1324.5</td>\n",
              "      <td>22</td>\n",
              "      <td>Monday</td>\n",
              "      <td>17191.700000</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5039</th>\n",
              "      <td>2015-01-26 23:00:00</td>\n",
              "      <td>243.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Monday</td>\n",
              "      <td>12863.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5040</th>\n",
              "      <td>2015-01-27 00:00:00</td>\n",
              "      <td>94.5</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>8483.709677</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5041</th>\n",
              "      <td>2015-01-27 01:00:00</td>\n",
              "      <td>39.5</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>5028.193548</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5047</th>\n",
              "      <td>2015-01-27 07:00:00</td>\n",
              "      <td>274.0</td>\n",
              "      <td>7</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>15314.741935</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5048</th>\n",
              "      <td>2015-01-27 08:00:00</td>\n",
              "      <td>809.5</td>\n",
              "      <td>8</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>18504.435484</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5062</th>\n",
              "      <td>2015-01-27 22:00:00</td>\n",
              "      <td>9840.0</td>\n",
              "      <td>22</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>20481.516129</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5063</th>\n",
              "      <td>2015-01-27 23:00:00</td>\n",
              "      <td>7792.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>16217.370968</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour    weekday     value_avg  day outliers\n",
              "1488 2014-09-01 00:00:00  13763.0     0     Monday   8020.283333    0      yes\n",
              "1631 2014-09-06 23:00:00  29418.5    23   Saturday  25192.516129    5      yes\n",
              "2640 2014-10-19 00:00:00  27146.5     0     Sunday  23898.733333    6      yes\n",
              "2977 2014-11-02 01:00:00  37204.5     1     Sunday  21593.533333    6      yes\n",
              "4271 2014-12-25 23:00:00   7977.5    23   Thursday  20732.241935    3      yes\n",
              "4413 2014-12-31 21:00:00  27559.5    21  Wednesday  23345.806452    2      yes\n",
              "4416 2015-01-01 00:00:00  25850.0     0   Thursday  12698.500000    3      yes\n",
              "4417 2015-01-01 01:00:00  29292.0     1   Thursday   8132.564516    3      yes\n",
              "4418 2015-01-01 02:00:00  25753.5     2   Thursday   5350.209677    3      yes\n",
              "4419 2015-01-01 03:00:00  22067.0     3   Thursday   3683.177419    3      yes\n",
              "4511 2015-01-04 23:00:00   8402.0    23     Sunday  11686.450000    6      yes\n",
              "4871 2015-01-19 23:00:00   9294.5    23     Monday  12863.000000    0      yes\n",
              "5015 2015-01-25 23:00:00   9077.0    23     Sunday  11686.450000    6      yes\n",
              "5033 2015-01-26 17:00:00   5221.0    17     Monday  17714.100000    0      yes\n",
              "5034 2015-01-26 18:00:00   5703.5    18     Monday  21190.150000    0      yes\n",
              "5035 2015-01-26 19:00:00   4565.5    19     Monday  21255.900000    0      yes\n",
              "5036 2015-01-26 20:00:00   3630.5    20     Monday  19880.766667    0      yes\n",
              "5037 2015-01-26 21:00:00   2907.0    21     Monday  19197.350000    0      yes\n",
              "5038 2015-01-26 22:00:00   1324.5    22     Monday  17191.700000    0      yes\n",
              "5039 2015-01-26 23:00:00    243.0    23     Monday  12863.000000    0      yes\n",
              "5040 2015-01-27 00:00:00     94.5     0    Tuesday   8483.709677    1      yes\n",
              "5041 2015-01-27 01:00:00     39.5     1    Tuesday   5028.193548    1      yes\n",
              "5047 2015-01-27 07:00:00    274.0     7    Tuesday  15314.741935    1      yes\n",
              "5048 2015-01-27 08:00:00    809.5     8    Tuesday  18504.435484    1      yes\n",
              "5062 2015-01-27 22:00:00   9840.0    22    Tuesday  20481.516129    1      yes\n",
              "5063 2015-01-27 23:00:00   7792.0    23    Tuesday  16217.370968    1      yes"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZymToAMZJPl3"
      },
      "source": [
        "Anomalous Point\r\n",
        "\r\n",
        "```\r\n",
        "NYC Marathon - 2014-11-02\r\n",
        "Thanksgiving - 2014-11-27\r\n",
        "Christmas - 2014-12-25\r\n",
        "New Years - 2015-01-01\r\n",
        "Snow Blizzard - 2015-01-26 and 2015-01-27```\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ppXs4w5Irjk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "52ca8277-81f7-4f26-d49b-aeacf9f69e53"
      },
      "source": [
        "df_final"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>value</th>\n",
              "      <th>hour</th>\n",
              "      <th>weekday</th>\n",
              "      <th>value_avg</th>\n",
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              "      <td>Tuesday</td>\n",
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              "      <td>no</td>\n",
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              "      <td>2014-07-01 03:00:00</td>\n",
              "      <td>2216.5</td>\n",
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              "      <td>Tuesday</td>\n",
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              "      <td>Saturday</td>\n",
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              "      <td>5</td>\n",
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            "text/plain": [
              "               timestamp    value  hour   weekday     value_avg  day outliers\n",
              "0    2014-07-01 00:00:00   9485.5     0   Tuesday   8483.709677    1       no\n",
              "1    2014-07-01 01:00:00   5433.0     1   Tuesday   5028.193548    1       no\n",
              "2    2014-07-01 02:00:00   3346.5     2   Tuesday   3052.112903    1       no\n",
              "3    2014-07-01 03:00:00   2216.5     3   Tuesday   2039.580645    1       no\n",
              "4    2014-07-01 04:00:00   2189.5     4   Tuesday   2031.258065    1       no\n",
              "...                  ...      ...   ...       ...           ...  ...      ...\n",
              "5155 2015-01-31 19:00:00  28288.5    19  Saturday  24501.870968    5       no\n",
              "5156 2015-01-31 20:00:00  24138.0    20  Saturday  22193.758065    5       no\n",
              "5157 2015-01-31 21:00:00  24194.5    21  Saturday  21983.241935    5       no\n",
              "5158 2015-01-31 22:00:00  26515.0    22  Saturday  23949.951613    5       no\n",
              "5159 2015-01-31 23:00:00  26439.5    23  Saturday  25192.516129    5       no\n",
              "\n",
              "[5160 rows x 7 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 30
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    {
      "cell_type": "code",
      "metadata": {
        "id": "cBUWAXzoJ7rH",
        "colab": {
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          "height": 542
        },
        "outputId": "389212d5-880e-458f-b749-02b1d5353468"
      },
      "source": [
        "fig = px.scatter(df_final, x='timestamp', y='value', color='outliers', hover_data=['weekday','hour','value_avg'], title='NYC Taxi Demand')\n",
        "\n",
        "fig.update_xaxes(\n",
        "    rangeslider_visible=True,\n",
        ")\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "code",
      "metadata": {
        "id": "sqZNyXV2OmkU"
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      "source": [
        "score=model.decision_function(data)"
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      "outputs": []
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        "outputId": "506aa0db-49fa-4ede-9081-7872199bbd07"
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      "execution_count": null,
      "outputs": [
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          "data": {
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              "array([0.11121194, 0.14408128, 0.16624801, ..., 0.14995021, 0.10888348,\n",
              "       0.09575327])"
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          "metadata": {
            "tags": []
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          "execution_count": 33
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      "metadata": {
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        "colab": {
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        "plt.hist(score, bins=50)"
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              "        -0.00527979,  0.00146267,  0.00820513,  0.01494759,  0.02169006,\n",
              "         0.02843252,  0.03517498,  0.04191744,  0.0486599 ,  0.05540236,\n",
              "         0.06214483,  0.06888729,  0.07562975,  0.08237221,  0.08911467,\n",
              "         0.09585713,  0.1025996 ,  0.10934206,  0.11608452,  0.12282698,\n",
              "         0.12956944,  0.1363119 ,  0.14305437,  0.14979683,  0.15653929,\n",
              "         0.16328175,  0.17002421,  0.17676667,  0.18350914,  0.1902516 ,\n",
              "         0.19699406]),\n",
              " <a list of 50 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdDP3nreOl2q"
      },
      "source": [
        "df_final['scores'] = score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZjGOKiEOll7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "faf8869f-e9aa-4f61-a5d6-0a5d27623df6"
      },
      "source": [
        "df_final"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>value</th>\n",
              "      <th>hour</th>\n",
              "      <th>weekday</th>\n",
              "      <th>value_avg</th>\n",
              "      <th>day</th>\n",
              "      <th>outliers</th>\n",
              "      <th>scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-07-01 00:00:00</td>\n",
              "      <td>9485.5</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>8483.709677</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>0.111212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-07-01 01:00:00</td>\n",
              "      <td>5433.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>5028.193548</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>0.144081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-07-01 02:00:00</td>\n",
              "      <td>3346.5</td>\n",
              "      <td>2</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>3052.112903</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>0.166248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-07-01 03:00:00</td>\n",
              "      <td>2216.5</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2039.580645</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>0.165087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-07-01 04:00:00</td>\n",
              "      <td>2189.5</td>\n",
              "      <td>4</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2031.258065</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>0.172817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5155</th>\n",
              "      <td>2015-01-31 19:00:00</td>\n",
              "      <td>28288.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>24501.870968</td>\n",
              "      <td>5</td>\n",
              "      <td>no</td>\n",
              "      <td>0.015320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5156</th>\n",
              "      <td>2015-01-31 20:00:00</td>\n",
              "      <td>24138.0</td>\n",
              "      <td>20</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>22193.758065</td>\n",
              "      <td>5</td>\n",
              "      <td>no</td>\n",
              "      <td>0.144910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5157</th>\n",
              "      <td>2015-01-31 21:00:00</td>\n",
              "      <td>24194.5</td>\n",
              "      <td>21</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>21983.241935</td>\n",
              "      <td>5</td>\n",
              "      <td>no</td>\n",
              "      <td>0.149950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5158</th>\n",
              "      <td>2015-01-31 22:00:00</td>\n",
              "      <td>26515.0</td>\n",
              "      <td>22</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>23949.951613</td>\n",
              "      <td>5</td>\n",
              "      <td>no</td>\n",
              "      <td>0.108883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5159</th>\n",
              "      <td>2015-01-31 23:00:00</td>\n",
              "      <td>26439.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>25192.516129</td>\n",
              "      <td>5</td>\n",
              "      <td>no</td>\n",
              "      <td>0.095753</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5160 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour  ... day  outliers    scores\n",
              "0    2014-07-01 00:00:00   9485.5     0  ...   1        no  0.111212\n",
              "1    2014-07-01 01:00:00   5433.0     1  ...   1        no  0.144081\n",
              "2    2014-07-01 02:00:00   3346.5     2  ...   1        no  0.166248\n",
              "3    2014-07-01 03:00:00   2216.5     3  ...   1        no  0.165087\n",
              "4    2014-07-01 04:00:00   2189.5     4  ...   1        no  0.172817\n",
              "...                  ...      ...   ...  ...  ..       ...       ...\n",
              "5155 2015-01-31 19:00:00  28288.5    19  ...   5        no  0.015320\n",
              "5156 2015-01-31 20:00:00  24138.0    20  ...   5        no  0.144910\n",
              "5157 2015-01-31 21:00:00  24194.5    21  ...   5        no  0.149950\n",
              "5158 2015-01-31 22:00:00  26515.0    22  ...   5        no  0.108883\n",
              "5159 2015-01-31 23:00:00  26439.5    23  ...   5        no  0.095753\n",
              "\n",
              "[5160 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0ASvjelOlQU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "outputId": "13dbecc3-fb72-4fd6-d65a-da9803715cb2"
      },
      "source": [
        "df_final.query('scores<-0.02')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>value</th>\n",
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              "  <tbody>\n",
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              "      <th>1488</th>\n",
              "      <td>2014-09-01 00:00:00</td>\n",
              "      <td>13763.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>8020.283333</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.038392</td>\n",
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              "      <td>2014-09-06 23:00:00</td>\n",
              "      <td>29418.5</td>\n",
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              "      <td>-0.034752</td>\n",
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              "      <th>2977</th>\n",
              "      <td>2014-11-02 01:00:00</td>\n",
              "      <td>37204.5</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>21593.533333</td>\n",
              "      <td>6</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.140129</td>\n",
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              "    <tr>\n",
              "      <th>4271</th>\n",
              "      <td>2014-12-25 23:00:00</td>\n",
              "      <td>7977.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>20732.241935</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.037014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4416</th>\n",
              "      <td>2015-01-01 00:00:00</td>\n",
              "      <td>25850.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>12698.500000</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.049553</td>\n",
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              "    <tr>\n",
              "      <th>4417</th>\n",
              "      <td>2015-01-01 01:00:00</td>\n",
              "      <td>29292.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>8132.564516</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.088218</td>\n",
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              "    <tr>\n",
              "      <th>4418</th>\n",
              "      <td>2015-01-01 02:00:00</td>\n",
              "      <td>25753.5</td>\n",
              "      <td>2</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>5350.209677</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.044011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4511</th>\n",
              "      <td>2015-01-04 23:00:00</td>\n",
              "      <td>8402.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>11686.450000</td>\n",
              "      <td>6</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.039599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5035</th>\n",
              "      <td>2015-01-26 19:00:00</td>\n",
              "      <td>4565.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Monday</td>\n",
              "      <td>21255.900000</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.024893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5036</th>\n",
              "      <td>2015-01-26 20:00:00</td>\n",
              "      <td>3630.5</td>\n",
              "      <td>20</td>\n",
              "      <td>Monday</td>\n",
              "      <td>19880.766667</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.036088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5037</th>\n",
              "      <td>2015-01-26 21:00:00</td>\n",
              "      <td>2907.0</td>\n",
              "      <td>21</td>\n",
              "      <td>Monday</td>\n",
              "      <td>19197.350000</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.053433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5038</th>\n",
              "      <td>2015-01-26 22:00:00</td>\n",
              "      <td>1324.5</td>\n",
              "      <td>22</td>\n",
              "      <td>Monday</td>\n",
              "      <td>17191.700000</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.080187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5039</th>\n",
              "      <td>2015-01-26 23:00:00</td>\n",
              "      <td>243.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Monday</td>\n",
              "      <td>12863.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.102603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5040</th>\n",
              "      <td>2015-01-27 00:00:00</td>\n",
              "      <td>94.5</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>8483.709677</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.047971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5063</th>\n",
              "      <td>2015-01-27 23:00:00</td>\n",
              "      <td>7792.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>16217.370968</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.031717</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour  ... day  outliers    scores\n",
              "1488 2014-09-01 00:00:00  13763.0     0  ...   0       yes -0.038392\n",
              "1631 2014-09-06 23:00:00  29418.5    23  ...   5       yes -0.034752\n",
              "2977 2014-11-02 01:00:00  37204.5     1  ...   6       yes -0.140129\n",
              "4271 2014-12-25 23:00:00   7977.5    23  ...   3       yes -0.037014\n",
              "4416 2015-01-01 00:00:00  25850.0     0  ...   3       yes -0.049553\n",
              "4417 2015-01-01 01:00:00  29292.0     1  ...   3       yes -0.088218\n",
              "4418 2015-01-01 02:00:00  25753.5     2  ...   3       yes -0.044011\n",
              "4511 2015-01-04 23:00:00   8402.0    23  ...   6       yes -0.039599\n",
              "5035 2015-01-26 19:00:00   4565.5    19  ...   0       yes -0.024893\n",
              "5036 2015-01-26 20:00:00   3630.5    20  ...   0       yes -0.036088\n",
              "5037 2015-01-26 21:00:00   2907.0    21  ...   0       yes -0.053433\n",
              "5038 2015-01-26 22:00:00   1324.5    22  ...   0       yes -0.080187\n",
              "5039 2015-01-26 23:00:00    243.0    23  ...   0       yes -0.102603\n",
              "5040 2015-01-27 00:00:00     94.5     0  ...   1       yes -0.047971\n",
              "5063 2015-01-27 23:00:00   7792.0    23  ...   1       yes -0.031717\n",
              "\n",
              "[15 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        ""
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      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        ""
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      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
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}