2.1_Anomaly_Detection_using_Isolation_Forest.ipynb

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        "Video walkthrough of the code : https://www.youtube.com/watch?v=hkXPdkPfgoo&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"
      ],
      "execution_count": 1,
      "outputs": []
    },
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        "id": "PJaoc_EcR1pB"
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      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/numenta/NAB/master/data/realKnownCause/nyc_taxi.csv')"
      ],
      "execution_count": 2,
      "outputs": []
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        "df['weekday']=pd.Categorical(df.timestamp.dt.strftime('%A'), categories=['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday'], ordered=True)"
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              "</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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iP4nsGOG_B0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "7ae9193d-74ea-40b6-b07e-39cffd64dd8a"
      },
      "source": [
        "df[['value','weekday']].groupby('weekday').mean().plot()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f066e5c1c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "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": "-4I-m1BsDALW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "5ea5d47a-47c1-4ac1-9962-852d03166e0d"
      },
      "source": [
        "df[['value','hour']].groupby('hour').mean().plot()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f066e495050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GURYuQ2tX9mE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "14511ba6-4b83-4949-ded9-11644fdb286d"
      },
      "source": [
        "fig = px.line(df.reset_index(), x='timestamp', y='value', title='NYC Taxi Demand')\n",
        "\n",
        "fig.update_xaxes(\n",
        "    rangeslider_visible=True,\n",
        ")\n",
        "fig.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "cell_type": "markdown",
      "metadata": {
        "id": "xfaGCvsSZdoy"
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      "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"
      ]
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    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
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        },
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      },
      "source": [
        "model =  IsolationForest(contamination=0.004)\n",
        "model.fit(df[['value']])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IsolationForest(behaviour='deprecated', bootstrap=False, contamination=0.004,\n",
              "                max_features=1.0, max_samples='auto', n_estimators=100,\n",
              "                n_jobs=None, random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
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          "execution_count": 17
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      "metadata": {
        "id": "LsrVVLVjIrmW"
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      "source": [
        "df['outliers']=pd.Series(model.predict(df[['value']])).apply(lambda x: 'yes' if (x == -1) else 'no' )"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynUws7-BMnf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
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        "outputId": "67d1d875-5680-46cc-94e3-4921473a9454"
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      "source": [
        "df.query('outliers==\"yes\"')"
      ],
      "execution_count": 19,
      "outputs": [
        {
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              "      <th>3479</th>\n",
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              "      <td>28299.0</td>\n",
              "      <td>23</td>\n",
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              "      <th>4417</th>\n",
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              "      <td>29292.0</td>\n",
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              "      <td>Thursday</td>\n",
              "      <td>yes</td>\n",
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              "      <th>4655</th>\n",
              "      <td>2015-01-10 23:00:00</td>\n",
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              "      <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",
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              "      <th>5040</th>\n",
              "      <td>2015-01-27 00:00:00</td>\n",
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              "      <th>5041</th>\n",
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              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
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              "    <tr>\n",
              "      <th>5044</th>\n",
              "      <td>2015-01-27 04:00:00</td>\n",
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              "      <td>4</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
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              "    <tr>\n",
              "      <th>5045</th>\n",
              "      <td>2015-01-27 05:00:00</td>\n",
              "      <td>29.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
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              "    <tr>\n",
              "      <th>5046</th>\n",
              "      <td>2015-01-27 06:00:00</td>\n",
              "      <td>88.0</td>\n",
              "      <td>6</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
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              "      <th>5047</th>\n",
              "      <td>2015-01-27 07:00:00</td>\n",
              "      <td>274.0</td>\n",
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              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
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              "      <td>2015-01-30 19:00:00</td>\n",
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              "      <th>5155</th>\n",
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              "      <td>28288.5</td>\n",
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              "      <td>yes</td>\n",
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              "               timestamp    value  hour   weekday outliers\n",
              "1631 2014-09-06 23:00:00  29418.5    23  Saturday      yes\n",
              "2135 2014-09-27 23:00:00  28024.5    23  Saturday      yes\n",
              "2639 2014-10-18 23:00:00  28253.5    23  Saturday      yes\n",
              "2971 2014-11-01 19:00:00  27912.0    19  Saturday      yes\n",
              "2977 2014-11-02 01:00:00  37204.5     1    Sunday      yes\n",
              "3143 2014-11-08 23:00:00  27926.0    23  Saturday      yes\n",
              "3311 2014-11-15 23:00:00  27713.0    23  Saturday      yes\n",
              "3479 2014-11-22 23:00:00  28299.0    23  Saturday      yes\n",
              "4417 2015-01-01 01:00:00  29292.0     1  Thursday      yes\n",
              "4655 2015-01-10 23:00:00  28351.0    23  Saturday      yes\n",
              "5039 2015-01-26 23:00:00    243.0    23    Monday      yes\n",
              "5040 2015-01-27 00:00:00     94.5     0   Tuesday      yes\n",
              "5041 2015-01-27 01:00:00     39.5     1   Tuesday      yes\n",
              "5042 2015-01-27 02:00:00     29.0     2   Tuesday      yes\n",
              "5043 2015-01-27 03:00:00      9.5     3   Tuesday      yes\n",
              "5044 2015-01-27 04:00:00     14.5     4   Tuesday      yes\n",
              "5045 2015-01-27 05:00:00     29.0     5   Tuesday      yes\n",
              "5046 2015-01-27 06:00:00     88.0     6   Tuesday      yes\n",
              "5047 2015-01-27 07:00:00    274.0     7   Tuesday      yes\n",
              "5131 2015-01-30 19:00:00  27698.0    19    Friday      yes\n",
              "5155 2015-01-31 19:00:00  28288.5    19  Saturday      yes"
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      "source": [
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      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 26
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      "execution_count": 20,
      "outputs": [
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          "output_type": "execute_result",
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              "      <td>0</td>\n",
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              "      <td>2014-07-01 01:00:00</td>\n",
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              "      <td>Tuesday</td>\n",
              "      <td>no</td>\n",
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              "    <tr>\n",
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              "      <td>3346.5</td>\n",
              "      <td>2</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>no</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",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>no</td>\n",
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              "    <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>no</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>5155</th>\n",
              "      <td>2015-01-31 19:00:00</td>\n",
              "      <td>28288.5</td>\n",
              "      <td>19</td>\n",
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              "      <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>no</td>\n",
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              "      <td>2015-01-31 21:00:00</td>\n",
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              "      <td>21</td>\n",
              "      <td>Saturday</td>\n",
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              "      <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>no</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>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5160 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour   weekday outliers\n",
              "0    2014-07-01 00:00:00   9485.5     0   Tuesday       no\n",
              "1    2014-07-01 01:00:00   5433.0     1   Tuesday       no\n",
              "2    2014-07-01 02:00:00   3346.5     2   Tuesday       no\n",
              "3    2014-07-01 03:00:00   2216.5     3   Tuesday       no\n",
              "4    2014-07-01 04:00:00   2189.5     4   Tuesday       no\n",
              "...                  ...      ...   ...       ...      ...\n",
              "5155 2015-01-31 19:00:00  28288.5    19  Saturday      yes\n",
              "5156 2015-01-31 20:00:00  24138.0    20  Saturday       no\n",
              "5157 2015-01-31 21:00:00  24194.5    21  Saturday       no\n",
              "5158 2015-01-31 22:00:00  26515.0    22  Saturday       no\n",
              "5159 2015-01-31 23:00:00  26439.5    23  Saturday       no\n",
              "\n",
              "[5160 rows x 5 columns]"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cBUWAXzoJ7rH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "outputId": "b8cb8b83-6b45-42da-bcc8-a2e30390cf77"
      },
      "source": [
        "fig = px.scatter(df.reset_index(), x='timestamp', y='value', color='outliers', hover_data=['weekday'], title='NYC Taxi Demand')\n",
        "\n",
        "fig.update_xaxes(\n",
        "    rangeslider_visible=True,\n",
        ")\n",
        "fig.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/plotly/express/_core.py:286: FutureWarning:\n",
            "\n",
            "Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('b2e6fa51-f963-458b-860c-9c55daca61ce');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        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",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WzsXpYmZK8Eq"
      },
      "source": [
        "Now let's train the model with default value of contamination"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AOKhQ5LRbn2T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46988e78-3493-4f4c-b19b-6f4f99aed343"
      },
      "source": [
        "model =  IsolationForest()\n",
        "model.fit(df[['value']])"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IsolationForest(behaviour='deprecated', bootstrap=False, contamination='auto',\n",
              "                max_features=1.0, max_samples='auto', n_estimators=100,\n",
              "                n_jobs=None, random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqZNyXV2OmkU"
      },
      "source": [
        "score=model.decision_function(df[['value']])"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYPDjWa1OmVs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f86c72e2-d3da-46ed-fb29-82145cfafbd3"
      },
      "source": [
        "score"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.01219322, -0.04782998, -0.00328975, ..., -0.03816764,\n",
              "       -0.11017764, -0.1029199 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZJ73LqpOl9N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "outputId": "9eab1ba2-246c-4803-e0a7-e9077e853194"
      },
      "source": [
        "plt.hist(score, bins=50)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  5.,   5.,   2.,   0.,   4.,  10.,   8.,   0.,   6.,   6.,   4.,\n",
              "          2.,   7.,  15.,  11.,  12.,   5.,  10.,   6.,  12.,  23.,  19.,\n",
              "         12.,  15.,  23.,  30.,   9.,  87., 200., 315., 246., 213., 160.,\n",
              "        172., 248., 426., 306., 374.,  87.,  90., 104., 217., 327., 114.,\n",
              "         75., 148., 160., 220., 426., 184.]),\n",
              " array([-0.2310602 , -0.22473926, -0.21841832, -0.21209738, -0.20577644,\n",
              "        -0.1994555 , -0.19313456, -0.18681362, -0.18049268, -0.17417174,\n",
              "        -0.1678508 , -0.16152985, -0.15520891, -0.14888797, -0.14256703,\n",
              "        -0.13624609, -0.12992515, -0.12360421, -0.11728327, -0.11096233,\n",
              "        -0.10464139, -0.09832045, -0.09199951, -0.08567856, -0.07935762,\n",
              "        -0.07303668, -0.06671574, -0.0603948 , -0.05407386, -0.04775292,\n",
              "        -0.04143198, -0.03511104, -0.0287901 , -0.02246916, -0.01614822,\n",
              "        -0.00982727, -0.00350633,  0.00281461,  0.00913555,  0.01545649,\n",
              "         0.02177743,  0.02809837,  0.03441931,  0.04074025,  0.04706119,\n",
              "         0.05338213,  0.05970307,  0.06602402,  0.07234496,  0.0786659 ,\n",
              "         0.08498684]),\n",
              " <a list of 50 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "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['scores'] = score"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZjGOKiEOll7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "9f28e193-8bd4-4369-b604-b972f9b2bf1e"
      },
      "source": [
        "df"
      ],
      "execution_count": 30,
      "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>outliers</th>\n",
              "      <th>scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-07-01 00:00:00</td>\n",
              "      <td>9485.5</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.012193</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>no</td>\n",
              "      <td>-0.047830</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>no</td>\n",
              "      <td>-0.003290</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>no</td>\n",
              "      <td>-0.057415</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>no</td>\n",
              "      <td>-0.064863</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>yes</td>\n",
              "      <td>-0.224168</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>no</td>\n",
              "      <td>-0.037138</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>no</td>\n",
              "      <td>-0.038168</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>no</td>\n",
              "      <td>-0.110178</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>no</td>\n",
              "      <td>-0.102920</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5160 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour   weekday outliers    scores\n",
              "0    2014-07-01 00:00:00   9485.5     0   Tuesday       no -0.012193\n",
              "1    2014-07-01 01:00:00   5433.0     1   Tuesday       no -0.047830\n",
              "2    2014-07-01 02:00:00   3346.5     2   Tuesday       no -0.003290\n",
              "3    2014-07-01 03:00:00   2216.5     3   Tuesday       no -0.057415\n",
              "4    2014-07-01 04:00:00   2189.5     4   Tuesday       no -0.064863\n",
              "...                  ...      ...   ...       ...      ...       ...\n",
              "5155 2015-01-31 19:00:00  28288.5    19  Saturday      yes -0.224168\n",
              "5156 2015-01-31 20:00:00  24138.0    20  Saturday       no -0.037138\n",
              "5157 2015-01-31 21:00:00  24194.5    21  Saturday       no -0.038168\n",
              "5158 2015-01-31 22:00:00  26515.0    22  Saturday       no -0.110178\n",
              "5159 2015-01-31 23:00:00  26439.5    23  Saturday       no -0.102920\n",
              "\n",
              "[5160 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0ASvjelOlQU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "25e9c2e2-a719-4f60-eecc-3f597a6da951"
      },
      "source": [
        "df.query('scores<-0.20')"
      ],
      "execution_count": 31,
      "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>outliers</th>\n",
              "      <th>scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\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>yes</td>\n",
              "      <td>-0.228098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2135</th>\n",
              "      <td>2014-09-27 23:00:00</td>\n",
              "      <td>28024.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.219285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2639</th>\n",
              "      <td>2014-10-18 23:00:00</td>\n",
              "      <td>28253.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.224168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2807</th>\n",
              "      <td>2014-10-25 23:00:00</td>\n",
              "      <td>27554.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.202047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2971</th>\n",
              "      <td>2014-11-01 19:00:00</td>\n",
              "      <td>27912.0</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.218799</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>yes</td>\n",
              "      <td>-0.231060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3143</th>\n",
              "      <td>2014-11-08 23:00:00</td>\n",
              "      <td>27926.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.219285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3311</th>\n",
              "      <td>2014-11-15 23:00:00</td>\n",
              "      <td>27713.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.213058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3479</th>\n",
              "      <td>2014-11-22 23:00:00</td>\n",
              "      <td>28299.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.226131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4413</th>\n",
              "      <td>2014-12-31 21:00:00</td>\n",
              "      <td>27559.5</td>\n",
              "      <td>21</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.202047</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>yes</td>\n",
              "      <td>-0.228098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4627</th>\n",
              "      <td>2015-01-09 19:00:00</td>\n",
              "      <td>27559.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Friday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.202047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4655</th>\n",
              "      <td>2015-01-10 23:00:00</td>\n",
              "      <td>28351.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.226131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4795</th>\n",
              "      <td>2015-01-16 19:00:00</td>\n",
              "      <td>27571.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Friday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.203474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5131</th>\n",
              "      <td>2015-01-30 19:00:00</td>\n",
              "      <td>27698.0</td>\n",
              "      <td>19</td>\n",
              "      <td>Friday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.212576</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>yes</td>\n",
              "      <td>-0.224168</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour    weekday outliers    scores\n",
              "1631 2014-09-06 23:00:00  29418.5    23   Saturday      yes -0.228098\n",
              "2135 2014-09-27 23:00:00  28024.5    23   Saturday      yes -0.219285\n",
              "2639 2014-10-18 23:00:00  28253.5    23   Saturday      yes -0.224168\n",
              "2807 2014-10-25 23:00:00  27554.5    23   Saturday       no -0.202047\n",
              "2971 2014-11-01 19:00:00  27912.0    19   Saturday      yes -0.218799\n",
              "2977 2014-11-02 01:00:00  37204.5     1     Sunday      yes -0.231060\n",
              "3143 2014-11-08 23:00:00  27926.0    23   Saturday      yes -0.219285\n",
              "3311 2014-11-15 23:00:00  27713.0    23   Saturday      yes -0.213058\n",
              "3479 2014-11-22 23:00:00  28299.0    23   Saturday      yes -0.226131\n",
              "4413 2014-12-31 21:00:00  27559.5    21  Wednesday       no -0.202047\n",
              "4417 2015-01-01 01:00:00  29292.0     1   Thursday      yes -0.228098\n",
              "4627 2015-01-09 19:00:00  27559.5    19     Friday       no -0.202047\n",
              "4655 2015-01-10 23:00:00  28351.0    23   Saturday      yes -0.226131\n",
              "4795 2015-01-16 19:00:00  27571.5    19     Friday       no -0.203474\n",
              "5131 2015-01-30 19:00:00  27698.0    19     Friday      yes -0.212576\n",
              "5155 2015-01-31 19:00:00  28288.5    19   Saturday      yes -0.224168"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eXzTCwXJKd8O"
      },
      "source": [
        "---------------------------------"
      ]
    }
  ]
}