2.2_Anomaly_Detection_using_Isolation_Forest_Feature_Engineering.ipynb

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        "![link text](https://www.anblicks.com/wp-content/uploads/2020/11/contextual-outlier.png)\n",
        "\n",
        "Image Source: anblick.com\n",
        "\n",
        "Video help for the code : https://www.youtube.com/watch?v=XCF-kqCB_vA&list=PL3N9eeOlCrP5DflJUcymWEKhfhLx2MhRO&index=5"
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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": []
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        "df = pd.read_csv('https://github.com/raw/gulabpatel/Anomaly_Detection_Python/master/dataset/nyc_taxi.csv')"
      ],
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        "fig = px.line(df.reset_index(), x='timestamp', y='value', title='NYC Taxi Demand')\n",
        "\n",
        "fig.update_xaxes(\n",
        "    rangeslider_visible=True,\n",
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{\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"NYC Taxi Demand\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"rangeslider\": {\"visible\": true}, \"title\": {\"text\": \"timestamp\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"value\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
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              "        if (!display || display === 'none') {{\n",
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
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              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\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": "dac6d6fa-8759-4baa-ba5e-4a56dae7ef3d"
      },
      "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": "bc7056b6-b826-4f38-87b5-069921bde5ed"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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",
              "    </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": 297
        },
        "outputId": "9da8bc7f-16ba-450a-cd8d-b1cb8726008f"
      },
      "source": [
        "df[['value','hour']].groupby('hour').mean().plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17d0fa80d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iP4nsGOG_B0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "ea37ee73-af8c-442b-f099-a96654f8edae"
      },
      "source": [
        "df[['value','weekday']].groupby('weekday').mean().plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17d06ff210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "QC84wuU9ZRwi",
        "outputId": "df29dac1-5fb8-446f-b356-d69698d4c77f"
      },
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "df.groupby(['hour', 'weekday']).mean()['value'].plot()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17cfd29e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "tmnDYdouo_n8",
        "outputId": "c7814993-f9ea-4bd9-d035-a0f09e082f7b"
      },
      "source": [
        "plt.figure(figsize=(30,10))\n",
        "df.groupby(['hour', 'weekday']).mean()['value'].unstack().plot()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17cfb6b0d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jeLoqzjuakQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "a81eec14-7320-4217-8581-7ae59d20c61a"
      },
      "source": [
        "df_final=df.join(df.groupby(['hour', 'weekday'])['value'].mean(), on=['hour','weekday'], rsuffix='_avg')\n",
        "df_final"
      ],
      "execution_count": 22,
      "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": 22
        }
      ]
    },
    {
      "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": "fb7100e4-f2a5-4b62-b480-b44602a7ab37"
      },
      "source": [
        "df_final.set_index('timestamp').loc['2014-11-02']"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>value</th>\n",
              "      <th>hour</th>\n",
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              "      <th>2014-11-02 00:00:00</th>\n",
              "      <td>24109.5</td>\n",
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              "      <td>Sunday</td>\n",
              "      <td>23898.733333</td>\n",
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              "      <th>2014-11-02 01:00:00</th>\n",
              "      <td>37204.5</td>\n",
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              "      <td>21593.533333</td>\n",
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              "      <th>2014-11-02 02:00:00</th>\n",
              "      <td>12754.5</td>\n",
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              "      <td>17555.266667</td>\n",
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              "    <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": 26
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xsFsT0Qz4_i3",
        "outputId": "eae2b2ec-0822-4b34-9254-108c554262fe"
      },
      "source": [
        "df_final.set_index('timestamp').loc['2015-01-26':'2015-01-27']"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>hour</th>\n",
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              "    <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": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDa85OpY6iYH"
      },
      "source": [
        "df_final['day']=df.timestamp.dt.weekday"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "2m54fFk-67dk",
        "outputId": "e0325088-7736-4cef-a9e1-9caeae3fe6ec"
      },
      "source": [
        "df_final"
      ],
      "execution_count": 29,
      "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",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AwLWcIxp7Gpa"
      },
      "source": [
        "data = df_final[['value', 'hour', 'day']]"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwvrxTpCHZF5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b12d6407-dfa2-48cc-8c58-73d10bce1ae5"
      },
      "source": [
        "model =  IsolationForest(contamination=0.005, max_features=3, max_samples=0.8, n_estimators=200)\n",
        "model.fit(data)"
      ],
      "execution_count": 31,
      "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": 31
        }
      ]
    },
    {
      "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": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynUws7-BMnf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 846
        },
        "outputId": "f0cc2065-6593-4093-e92d-3a19559029b4"
      },
      "source": [
        "df_final.query('outliers==\"yes\"')"
      ],
      "execution_count": 33,
      "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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              "    }\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>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>5031</th>\n",
              "      <td>2015-01-26 15:00:00</td>\n",
              "      <td>8670.0</td>\n",
              "      <td>15</td>\n",
              "      <td>Monday</td>\n",
              "      <td>16831.283333</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5032</th>\n",
              "      <td>2015-01-26 16:00:00</td>\n",
              "      <td>5888.5</td>\n",
              "      <td>16</td>\n",
              "      <td>Monday</td>\n",
              "      <td>14833.983333</td>\n",
              "      <td>0</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>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",
              "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",
              "5031 2015-01-26 15:00:00   8670.0    15    Monday  16831.283333    0      yes\n",
              "5032 2015-01-26 16:00:00   5888.5    16    Monday  14833.983333    0      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",
              "5063 2015-01-27 23:00:00   7792.0    23   Tuesday  16217.370968    1      yes"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZymToAMZJPl3"
      },
      "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": {
        "id": "9ppXs4w5Irjk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "a1be5e9c-0dc1-4166-eb66-da5069ba4283"
      },
      "source": [
        "df_final"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
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              "      <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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
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              "    <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",
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              "<p>5160 rows × 7 columns</p>\n",
              "</div>"
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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]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cBUWAXzoJ7rH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "8e72f9c7-1342-472a-9360-d6b3a63a7eee"
      },
      "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": 35,
      "outputs": [
        {
          "output_type": "display_data",
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              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
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              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })\n",
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              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDq0PWfuq2D1"
      },
      "source": [
        "model_decision_function() gives you score instead of 1 0r -1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqZNyXV2OmkU"
      },
      "source": [
        "score=model.decision_function(data)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYPDjWa1OmVs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "867085ff-0a05-43d1-fa0f-741bf2e536db"
      },
      "source": [
        "score"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.1064084 , 0.14624986, 0.16663447, ..., 0.14561792, 0.11634174,\n",
              "       0.09733991])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZJ73LqpOl9N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "0559f0ec-811e-49e9-a2bb-e5b54b5e1468"
      },
      "source": [
        "plt.hist(score, bins=50)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  1.,   0.,   0.,   0.,   0.,   0.,   1.,   1.,   0.,   0.,   1.,\n",
              "          0.,   0.,   1.,   2.,   2.,   4.,   2.,   4.,   1.,   6.,   3.,\n",
              "          6.,  10.,  11.,  18.,  21.,  24.,  34.,  34.,  53.,  85., 100.,\n",
              "         99., 125., 130., 229., 263., 272., 351., 405., 467., 494., 455.,\n",
              "        406., 343., 200., 204., 193.,  99.]),\n",
              " array([-1.49749045e-01, -1.42616312e-01, -1.35483579e-01, -1.28350847e-01,\n",
              "        -1.21218114e-01, -1.14085381e-01, -1.06952649e-01, -9.98199160e-02,\n",
              "        -9.26871834e-02, -8.55544507e-02, -7.84217181e-02, -7.12889854e-02,\n",
              "        -6.41562528e-02, -5.70235201e-02, -4.98907875e-02, -4.27580548e-02,\n",
              "        -3.56253221e-02, -2.84925895e-02, -2.13598568e-02, -1.42271242e-02,\n",
              "        -7.09439152e-03,  3.83411343e-05,  7.17107379e-03,  1.43038064e-02,\n",
              "         2.14365391e-02,  2.85692718e-02,  3.57020044e-02,  4.28347371e-02,\n",
              "         4.99674697e-02,  5.71002024e-02,  6.42329350e-02,  7.13656677e-02,\n",
              "         7.84984003e-02,  8.56311330e-02,  9.27638656e-02,  9.98965983e-02,\n",
              "         1.07029331e-01,  1.14162064e-01,  1.21294796e-01,  1.28427529e-01,\n",
              "         1.35560262e-01,  1.42692994e-01,  1.49825727e-01,  1.56958460e-01,\n",
              "         1.64091192e-01,  1.71223925e-01,  1.78356658e-01,  1.85489390e-01,\n",
              "         1.92622123e-01,  1.99754855e-01,  2.06887588e-01]),\n",
              " <a list of 50 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdDP3nreOl2q"
      },
      "source": [
        "df_final['scores'] = score"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZjGOKiEOll7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "1478a751-086c-42fc-bb18-89e202ebe537"
      },
      "source": [
        "df_final"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
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              "      <th>scores</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-07-01 00:00:00</td>\n",
              "      <td>9485.5</td>\n",
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              "      <td>8483.709677</td>\n",
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              "      <td>no</td>\n",
              "      <td>0.106408</td>\n",
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              "    <tr>\n",
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              "      <td>2014-07-01 01:00:00</td>\n",
              "      <td>5433.0</td>\n",
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              "      <td>Tuesday</td>\n",
              "      <td>5028.193548</td>\n",
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              "      <td>no</td>\n",
              "      <td>0.146250</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",
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              "      <td>no</td>\n",
              "      <td>0.166634</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-07-01 03:00:00</td>\n",
              "      <td>2216.5</td>\n",
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              "      <td>Tuesday</td>\n",
              "      <td>2039.580645</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>0.168622</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>0.173190</td>\n",
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              "      <th>5156</th>\n",
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              "      <td>22193.758065</td>\n",
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              "    <tr>\n",
              "      <th>5157</th>\n",
              "      <td>2015-01-31 21:00:00</td>\n",
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              "    <tr>\n",
              "      <th>5158</th>\n",
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              "      <td>25192.516129</td>\n",
              "      <td>5</td>\n",
              "      <td>no</td>\n",
              "      <td>0.097340</td>\n",
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              "  </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  0.106408\n",
              "1    2014-07-01 01:00:00   5433.0     1  ...   1        no  0.146250\n",
              "2    2014-07-01 02:00:00   3346.5     2  ...   1        no  0.166634\n",
              "3    2014-07-01 03:00:00   2216.5     3  ...   1        no  0.168622\n",
              "4    2014-07-01 04:00:00   2189.5     4  ...   1        no  0.173190\n",
              "...                  ...      ...   ...  ...  ..       ...       ...\n",
              "5155 2015-01-31 19:00:00  28288.5    19  ...   5        no  0.021296\n",
              "5156 2015-01-31 20:00:00  24138.0    20  ...   5        no  0.148347\n",
              "5157 2015-01-31 21:00:00  24194.5    21  ...   5        no  0.145618\n",
              "5158 2015-01-31 22:00:00  26515.0    22  ...   5        no  0.116342\n",
              "5159 2015-01-31 23:00:00  26439.5    23  ...   5        no  0.097340\n",
              "\n",
              "[5160 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0ASvjelOlQU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "outputId": "77500cfa-ea85-43b4-a61b-d1c045e7d751"
      },
      "source": [
        "df_final.query('scores<-0.02')"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>1488</th>\n",
              "      <td>2014-09-01 00:00:00</td>\n",
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              "      <td>0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>8020.283333</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.026639</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",
              "      <td>-0.033168</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",
              "      <td>-0.020988</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",
              "      <td>-0.149749</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",
              "      <td>-0.025584</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>-0.040697</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",
              "      <td>-0.095463</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",
              "      <td>-0.044942</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>-0.030301</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>-0.029814</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>-0.038319</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>-0.052779</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>-0.076658</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>-0.101883</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>-0.044360</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>-0.029254</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 -0.026639\n",
              "1631 2014-09-06 23:00:00  29418.5    23  ...   5       yes -0.033168\n",
              "2640 2014-10-19 00:00:00  27146.5     0  ...   6       yes -0.020988\n",
              "2977 2014-11-02 01:00:00  37204.5     1  ...   6       yes -0.149749\n",
              "4271 2014-12-25 23:00:00   7977.5    23  ...   3       yes -0.025584\n",
              "4416 2015-01-01 00:00:00  25850.0     0  ...   3       yes -0.040697\n",
              "4417 2015-01-01 01:00:00  29292.0     1  ...   3       yes -0.095463\n",
              "4418 2015-01-01 02:00:00  25753.5     2  ...   3       yes -0.044942\n",
              "4511 2015-01-04 23:00:00   8402.0    23  ...   6       yes -0.030301\n",
              "5035 2015-01-26 19:00:00   4565.5    19  ...   0       yes -0.029814\n",
              "5036 2015-01-26 20:00:00   3630.5    20  ...   0       yes -0.038319\n",
              "5037 2015-01-26 21:00:00   2907.0    21  ...   0       yes -0.052779\n",
              "5038 2015-01-26 22:00:00   1324.5    22  ...   0       yes -0.076658\n",
              "5039 2015-01-26 23:00:00    243.0    23  ...   0       yes -0.101883\n",
              "5040 2015-01-27 00:00:00     94.5     0  ...   1       yes -0.044360\n",
              "5063 2015-01-27 23:00:00   7792.0    23  ...   1       yes -0.029254\n",
              "\n",
              "[16 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLoQdp58qCtS"
      },
      "source": [
        "-----------------"
      ]
    }
  ]
}