Data_Analysis_in_Zuber_RideSharing_Company.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"KFKvsOc9tyFD"},"source":["# Stage 4. Exploratory Data Analysis\n","\n","In addition to the data that you obtained in the previous task, you have been given a second file. Now, you have the following two CSV files:\n","\n","`/content/project_sql_result_01.csv` - contains the following data:\n","\n","`company_name`: the name of the taxi company\n","\n","`trips_amount`: number of trips for each taxi company on 15-16 November 2017.\n","\n","`/content/project_sql_result_04.csv` - contains the following data:\n","\n","`dropoff_location_name`: the name of the Chicago area where the trip ends\n","\n","`average_trips`: the average number of trips ending in each region in November 2017.\n","\n","For both datasets, you will now need to\n","- import both files\n","- study the contents of the data\n","- make sure the data type is correct\n","- identify the top 10 regions to serve as delivery points\n","- charting: taxi companies and their number of trips, top 10 regions by number of drop offs\n","- draw conclusions based on the graphs that have been made and explain the results"]},{"cell_type":"markdown","metadata":{"id":"KmPhNBqYtyFG"},"source":["# 4.1 Import both files"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fLe3rUsNtyFG"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","from scipy import stats as st\n","\n","import warnings\n","warnings.filterwarnings('ignore')\n","\n","#for importing files to google collab\n","from google.colab import files"]},{"cell_type":"code","source":["#upload datasets\n","#uploaded = files.upload()"],"metadata":{"id":"vpP2T0l6rUWs"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zUnqE0NutyFG"},"outputs":[],"source":["company_df = pd.read_csv('/content/project_sql_result_01.csv')\n","trips_df = pd.read_csv('/content/project_sql_result_04.csv')"]},{"cell_type":"markdown","metadata":{"id":"QYcrJAo8tyFH"},"source":["# 4.2 Learn and Load the Data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"03qKqumJtyFH","outputId":"5a625d4f-66fa-426b-f57d-200cc8362343","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1689930784767,"user_tz":-420,"elapsed":20,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                      company_name  trips_amount\n","0                        Flash Cab         19558\n","1        Taxi Affiliation Services         11422\n","2                 Medallion Leasin         10367\n","3                       Yellow Cab          9888\n","4  Taxi Affiliation Service Yellow          9299"],"text/html":["\n","\n","  <div id=\"df-e4dd25fd-77e2-491a-bcd4-12207814e28e\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>company_name</th>\n","      <th>trips_amount</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Flash Cab</td>\n","      <td>19558</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Taxi Affiliation Services</td>\n","      <td>11422</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Medallion Leasin</td>\n","      <td>10367</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Yellow Cab</td>\n","      <td>9888</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Taxi Affiliation Service Yellow</td>\n","      <td>9299</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4dd25fd-77e2-491a-bcd4-12207814e28e')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-9ee4ccad-524e-4dbe-926d-0bf72028e1b8\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9ee4ccad-524e-4dbe-926d-0bf72028e1b8')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-9ee4ccad-524e-4dbe-926d-0bf72028e1b8 button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-e4dd25fd-77e2-491a-bcd4-12207814e28e button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-e4dd25fd-77e2-491a-bcd4-12207814e28e');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":6}],"source":["company_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6d8d0MS5tyFH","outputId":"fdb787e1-8ac8-49e2-d99e-9fa2ade2d95d","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930784768,"user_tz":-420,"elapsed":19,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(64, 2)"]},"metadata":{},"execution_count":7}],"source":["company_df.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"z3cRKl48tyFH","outputId":"dc853d01-687f-4686-b21a-baf113aaebb4","colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"status":"ok","timestamp":1689930784768,"user_tz":-420,"elapsed":15,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["       trips_amount\n","count     64.000000\n","mean    2145.484375\n","std     3812.310186\n","min        2.000000\n","25%       20.750000\n","50%      178.500000\n","75%     2106.500000\n","max    19558.000000"],"text/html":["\n","\n","  <div id=\"df-202e47cd-c6fc-48b0-ae5a-31fadbc92dc2\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>trips_amount</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>64.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>2145.484375</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>3812.310186</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>2.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>20.750000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>178.500000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>2106.500000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>19558.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-202e47cd-c6fc-48b0-ae5a-31fadbc92dc2')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-96450964-4130-438c-9a9a-3c7b622f506f\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-96450964-4130-438c-9a9a-3c7b622f506f')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-96450964-4130-438c-9a9a-3c7b622f506f button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-202e47cd-c6fc-48b0-ae5a-31fadbc92dc2 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-202e47cd-c6fc-48b0-ae5a-31fadbc92dc2');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":8}],"source":["company_df.describe()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TO_AUfWMtyFI","outputId":"e42e7b7e-da99-4b80-a608-393b225db26d","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930784769,"user_tz":-420,"elapsed":14,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 64 entries, 0 to 63\n","Data columns (total 2 columns):\n"," #   Column        Non-Null Count  Dtype \n","---  ------        --------------  ----- \n"," 0   company_name  64 non-null     object\n"," 1   trips_amount  64 non-null     int64 \n","dtypes: int64(1), object(1)\n","memory usage: 1.1+ KB\n"]}],"source":["company_df.info()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cEuHt181tyFI","executionInfo":{"status":"ok","timestamp":1689930785223,"user_tz":-420,"elapsed":464,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}},"outputId":"678973a9-d852-476e-9648-6be7e8f4741e","colab":{"base_uri":"https://localhost:8080/"}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["company_name    0\n","trips_amount    0\n","dtype: int64"]},"metadata":{},"execution_count":10}],"source":["company_df.isnull().sum()"]},{"cell_type":"markdown","metadata":{"id":"-awgiKeltyFI"},"source":["**Conclusion**\n","\n","Following are the findings on the `project_sql_result_01.csv` dataset\n","- There are 64 rows of data, and 2 columns\n","- Has a mean of 2145 in `trips_amount`\n","- Has a median of 178.5 in `trips_amount`\n","- the minimum `trips_amount` is 2\n","- The maximum `trips_amount` is 19558\n","- There is no missing value (NULL) has been found"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uuPLREp0tyFJ","outputId":"1f91a643-5850-4a31-cd6b-7ba27c61bd03","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1689930785224,"user_tz":-420,"elapsed":32,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["  dropoff_location_name  average_trips\n","0                  Loop   10727.466667\n","1           River North    9523.666667\n","2         Streeterville    6664.666667\n","3             West Loop    5163.666667\n","4                O'Hare    2546.900000"],"text/html":["\n","\n","  <div id=\"df-00d15ea4-f331-468d-8f54-6029f796a20a\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>dropoff_location_name</th>\n","      <th>average_trips</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Loop</td>\n","      <td>10727.466667</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>River North</td>\n","      <td>9523.666667</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Streeterville</td>\n","      <td>6664.666667</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>West Loop</td>\n","      <td>5163.666667</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>O'Hare</td>\n","      <td>2546.900000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00d15ea4-f331-468d-8f54-6029f796a20a')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-e9f3b77d-2430-4857-ada4-1125d38089b6\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e9f3b77d-2430-4857-ada4-1125d38089b6')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-e9f3b77d-2430-4857-ada4-1125d38089b6 button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-00d15ea4-f331-468d-8f54-6029f796a20a button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-00d15ea4-f331-468d-8f54-6029f796a20a');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":11}],"source":["trips_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"86-aLGZttyFJ","outputId":"d03de778-653a-4608-abbd-c60bcad393ae","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930785224,"user_tz":-420,"elapsed":31,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(94, 2)"]},"metadata":{},"execution_count":12}],"source":["trips_df.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Cx74HzXOtyFJ","outputId":"e83304a2-7e60-420b-df34-57a9f7e8f181","colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"status":"ok","timestamp":1689930785225,"user_tz":-420,"elapsed":30,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["       average_trips\n","count      94.000000\n","mean      599.953728\n","std      1714.591098\n","min         1.800000\n","25%        14.266667\n","50%        52.016667\n","75%       298.858333\n","max     10727.466667"],"text/html":["\n","\n","  <div id=\"df-f2ed3267-37b4-4f89-80c8-8a743c14a5cf\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>average_trips</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>94.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>599.953728</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1714.591098</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.800000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>14.266667</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>52.016667</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>298.858333</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>10727.466667</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2ed3267-37b4-4f89-80c8-8a743c14a5cf')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-03f139f0-81cd-4539-bfb2-50ea678a4c5f\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-03f139f0-81cd-4539-bfb2-50ea678a4c5f')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-03f139f0-81cd-4539-bfb2-50ea678a4c5f button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-f2ed3267-37b4-4f89-80c8-8a743c14a5cf button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-f2ed3267-37b4-4f89-80c8-8a743c14a5cf');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":13}],"source":["trips_df.describe()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rNt1wVUhtyFJ","outputId":"86442fef-1888-404b-96fc-090bb06ad0c0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930785225,"user_tz":-420,"elapsed":28,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 94 entries, 0 to 93\n","Data columns (total 2 columns):\n"," #   Column                 Non-Null Count  Dtype  \n","---  ------                 --------------  -----  \n"," 0   dropoff_location_name  94 non-null     object \n"," 1   average_trips          94 non-null     float64\n","dtypes: float64(1), object(1)\n","memory usage: 1.6+ KB\n"]}],"source":["trips_df.info()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fiuaaJP6tyFJ","outputId":"83bd9574-bd33-4e5a-f3a9-b6bea5abbe4f","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930785226,"user_tz":-420,"elapsed":27,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["dropoff_location_name    0\n","average_trips            0\n","dtype: int64"]},"metadata":{},"execution_count":15}],"source":["trips_df.isnull().sum()"]},{"cell_type":"markdown","metadata":{"id":"ZO11UIYStyFK"},"source":["**Conclusion**\n","\n","Here are the findings on the `/content/project_sql_result_04.csv` dataset\n","\n","- There are 94 rows of data, and 2 columns\n","- Has a mean of 600 in `average_trips`\n","- Has a median of 52 in `average_trips`\n","- the smallest `average_trips` is 1714\n","- The most `average_trips` is 10727\n","- There is no missing values (NULL) has been found"]},{"cell_type":"markdown","metadata":{"id":"Qa3dSIj_tyFK"},"source":["# 4.3 Ensure the datatype is correct"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7tB162zvtyFK","outputId":"7691cee5-d312-499e-b5d7-7c5ed3a71dce","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1689930785226,"user_tz":-420,"elapsed":25,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                      company_name  trips_amount\n","0                        Flash Cab         19558\n","1        Taxi Affiliation Services         11422\n","2                 Medallion Leasin         10367\n","3                       Yellow Cab          9888\n","4  Taxi Affiliation Service Yellow          9299"],"text/html":["\n","\n","  <div id=\"df-9f6355c9-b7e0-402c-8688-4935aebe29a8\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>company_name</th>\n","      <th>trips_amount</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Flash Cab</td>\n","      <td>19558</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Taxi Affiliation Services</td>\n","      <td>11422</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Medallion Leasin</td>\n","      <td>10367</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Yellow Cab</td>\n","      <td>9888</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Taxi Affiliation Service Yellow</td>\n","      <td>9299</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f6355c9-b7e0-402c-8688-4935aebe29a8')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-da65a941-04f6-4988-a3f8-b6dc6e6ca937\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da65a941-04f6-4988-a3f8-b6dc6e6ca937')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-da65a941-04f6-4988-a3f8-b6dc6e6ca937 button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-9f6355c9-b7e0-402c-8688-4935aebe29a8 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-9f6355c9-b7e0-402c-8688-4935aebe29a8');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":16}],"source":["company_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"g0YNnJZStyFK","outputId":"9de850b2-5c89-4971-e6f2-93302996e5e4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930785226,"user_tz":-420,"elapsed":24,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["company_name    object\n","trips_amount     int64\n","dtype: object"]},"metadata":{},"execution_count":17}],"source":["company_df.dtypes"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AxIfvJEYtyFL","outputId":"b438a894-6676-4853-eb45-41c8279f57d7","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1689930785227,"user_tz":-420,"elapsed":22,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["  dropoff_location_name  average_trips\n","0                  Loop   10727.466667\n","1           River North    9523.666667\n","2         Streeterville    6664.666667\n","3             West Loop    5163.666667\n","4                O'Hare    2546.900000"],"text/html":["\n","\n","  <div id=\"df-70c0d360-c470-4ca0-8d86-4b3bff256abe\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>dropoff_location_name</th>\n","      <th>average_trips</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Loop</td>\n","      <td>10727.466667</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>River North</td>\n","      <td>9523.666667</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Streeterville</td>\n","      <td>6664.666667</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>West Loop</td>\n","      <td>5163.666667</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>O'Hare</td>\n","      <td>2546.900000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70c0d360-c470-4ca0-8d86-4b3bff256abe')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-5ccbd024-3446-4d8f-bb2c-4d1374db0358\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5ccbd024-3446-4d8f-bb2c-4d1374db0358')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-5ccbd024-3446-4d8f-bb2c-4d1374db0358 button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-70c0d360-c470-4ca0-8d86-4b3bff256abe button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-70c0d360-c470-4ca0-8d86-4b3bff256abe');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":18}],"source":["trips_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OuPnQMuxtyFL","outputId":"12ecca4a-8bfb-4ae3-d8f6-14764920436b","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930785227,"user_tz":-420,"elapsed":22,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["dropoff_location_name     object\n","average_trips            float64\n","dtype: object"]},"metadata":{},"execution_count":19}],"source":["trips_df.dtypes"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b00AWch2tyFL"},"outputs":[],"source":["#there is an incorrect datatype, convert float to int\n","trips_df['average_trips'] = trips_df['average_trips'].apply(np.ceil).astype('int')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Xnkkqcg4tyFL","outputId":"b6824320-2217-48bf-c985-7de1892b4c96","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930785227,"user_tz":-420,"elapsed":19,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["dropoff_location_name    object\n","average_trips             int64\n","dtype: object"]},"metadata":{},"execution_count":21}],"source":["trips_df.dtypes"]},{"cell_type":"markdown","metadata":{"id":"8alMvKsftyFL"},"source":["**Conclusion**\n","\n","- Convert the `average_trips` datatype in the `trips_df` dataset to `int`, to be the same as the `trips_amount` datatype in the `company_df` dataset"]},{"cell_type":"markdown","metadata":{"id":"6bhLPTJVtyFL"},"source":["# 4.4 identifies the top 10 regions to serve as delivery points"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rnNKKASptyFL"},"outputs":[],"source":["top_10_dropoff = trips_df.sort_values('average_trips', ascending=False).head(10)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8gva4yb2tyFL","outputId":"7f2db197-cd74-462a-8af1-7820b5e4c73d","colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"status":"ok","timestamp":1689930785228,"user_tz":-420,"elapsed":19,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["  dropoff_location_name  average_trips\n","0                  Loop          10728\n","1           River North           9524\n","2         Streeterville           6665\n","3             West Loop           5164\n","4                O'Hare           2547\n","5             Lake View           2421\n","6            Grant Park           2069\n","7         Museum Campus           1510\n","8            Gold Coast           1365\n","9    Sheffield & DePaul           1260"],"text/html":["\n","\n","  <div id=\"df-d195835a-e976-4f21-a164-146d5582a4dc\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>dropoff_location_name</th>\n","      <th>average_trips</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Loop</td>\n","      <td>10728</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>River North</td>\n","      <td>9524</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Streeterville</td>\n","      <td>6665</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>West Loop</td>\n","      <td>5164</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>O'Hare</td>\n","      <td>2547</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Lake View</td>\n","      <td>2421</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Grant Park</td>\n","      <td>2069</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Museum Campus</td>\n","      <td>1510</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Gold Coast</td>\n","      <td>1365</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Sheffield &amp; DePaul</td>\n","      <td>1260</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d195835a-e976-4f21-a164-146d5582a4dc')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-5fef2fe7-b967-42fa-bf9c-00a7600bc0fc\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5fef2fe7-b967-42fa-bf9c-00a7600bc0fc')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-5fef2fe7-b967-42fa-bf9c-00a7600bc0fc button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-d195835a-e976-4f21-a164-146d5582a4dc button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-d195835a-e976-4f21-a164-146d5582a4dc');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":23}],"source":["top_10_dropoff"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b3nH1P4ktyFM","outputId":"8d916023-1ff4-4f67-e3c1-d93afe3d1909","colab":{"base_uri":"https://localhost:8080/","height":783},"executionInfo":{"status":"ok","timestamp":1689930785716,"user_tz":-420,"elapsed":506,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x800 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.figure(figsize=(10,8))\n","sns.boxplot (data=top_10_dropoff, x='dropoff_location_name', y='average_trips', orient='v')\n","plt.xticks(rotation=45);"]},{"cell_type":"markdown","metadata":{"id":"RAwG_pIGtyFM"},"source":["**Conclusion**\n","\n","- The most popular dropoff location is occupied by Loop, with an `average_trips` at 10728\n","- Meanwhile, position no. 10 is occupied by Sheffield & DePaul, with an `average_trips` at 1260"]},{"cell_type":"markdown","metadata":{"id":"UHxQu838tyFM"},"source":["# 4.5 create a chart: taxi companies and their number of trips with top 10 regions by number of drop offs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OPh_fJMVtyFM","outputId":"9bf01217-137b-44aa-8de7-c177d94f44a9","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1689930785717,"user_tz":-420,"elapsed":13,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                      company_name  trips_amount\n","0                        Flash Cab         19558\n","1        Taxi Affiliation Services         11422\n","2                 Medallion Leasin         10367\n","3                       Yellow Cab          9888\n","4  Taxi Affiliation Service Yellow          9299"],"text/html":["\n","\n","  <div id=\"df-c7d93908-d1c1-4ff3-ac61-5bb965cfce47\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>company_name</th>\n","      <th>trips_amount</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Flash Cab</td>\n","      <td>19558</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Taxi Affiliation Services</td>\n","      <td>11422</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Medallion Leasin</td>\n","      <td>10367</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Yellow Cab</td>\n","      <td>9888</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Taxi Affiliation Service Yellow</td>\n","      <td>9299</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7d93908-d1c1-4ff3-ac61-5bb965cfce47')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-5611361c-3370-4858-932b-9c17dbd81315\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5611361c-3370-4858-932b-9c17dbd81315')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-5611361c-3370-4858-932b-9c17dbd81315 button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-c7d93908-d1c1-4ff3-ac61-5bb965cfce47 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-c7d93908-d1c1-4ff3-ac61-5bb965cfce47');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":25}],"source":["company_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aTvt2scdtyFM"},"outputs":[],"source":["top_10_companies = company_df.sort_values('trips_amount', ascending=False).head(10)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kHHzk3YstyFM","outputId":"2e1cecb0-3a0b-44e6-81fc-88018acf4722","colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"status":"ok","timestamp":1689930785717,"user_tz":-420,"elapsed":12,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                        company_name  trips_amount\n","0                          Flash Cab         19558\n","1          Taxi Affiliation Services         11422\n","2                   Medallion Leasin         10367\n","3                         Yellow Cab          9888\n","4    Taxi Affiliation Service Yellow          9299\n","5          Chicago Carriage Cab Corp          9181\n","6                       City Service          8448\n","7                           Sun Taxi          7701\n","8          Star North Management LLC          7455\n","9  Blue Ribbon Taxi Association Inc.          5953"],"text/html":["\n","\n","  <div id=\"df-413e9822-409f-442c-ab65-d76d27d54e61\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>company_name</th>\n","      <th>trips_amount</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Flash Cab</td>\n","      <td>19558</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Taxi Affiliation Services</td>\n","      <td>11422</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Medallion Leasin</td>\n","      <td>10367</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Yellow Cab</td>\n","      <td>9888</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Taxi Affiliation Service Yellow</td>\n","      <td>9299</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Chicago Carriage Cab Corp</td>\n","      <td>9181</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>City Service</td>\n","      <td>8448</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Sun Taxi</td>\n","      <td>7701</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Star North Management LLC</td>\n","      <td>7455</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Blue Ribbon Taxi Association Inc.</td>\n","      <td>5953</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-413e9822-409f-442c-ab65-d76d27d54e61')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-aca85c27-2895-4b10-afc6-b07124455bec\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-aca85c27-2895-4b10-afc6-b07124455bec')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-aca85c27-2895-4b10-afc6-b07124455bec button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-413e9822-409f-442c-ab65-d76d27d54e61 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-413e9822-409f-442c-ab65-d76d27d54e61');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":27}],"source":["top_10_companies"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BSdElpp4tyFM","outputId":"810a696a-2dfb-4f61-eae1-bf70b97db7ee","colab":{"base_uri":"https://localhost:8080/","height":450},"executionInfo":{"status":"ok","timestamp":1689930786190,"user_tz":-420,"elapsed":484,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAw0AAAGxCAYAAAAgdlrHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACB6klEQVR4nOzdeVxO6f8/8NfRcrcXSYWStGpTso+yTlmyb8mSfV+GbB87g6xjHdsg+zayjLEMkVG2orKUCImRYVApJHV+f/h1vm7VXSpCr+fjcR66z3Wd67yvc9J9v+9zXecIoiiKICIiIiIiykOZkg6AiIiIiIi+bkwaiIiIiIhIISYNRERERESkEJMGIiIiIiJSiEkDEREREREpxKSBiIiIiIgUYtJAREREREQKMWkgIiIiIiKFlEs6ACL6+mVlZeHRo0fQ1taGIAglHQ4REREVgCiKePnyJSpWrIgyZYp2rYBJAxHl69GjRzAxMSnpMIiIiKgQHjx4gMqVKxepDSYNRJQvbW1tAO//6Ojo6JRwNERERFQQKSkpMDExkd7Hi4JJAxHlK3tIko6ODpMGIiKib0xxDC3mRGgiIiIiIlKIVxqIqMDcpuyEkky9pMMgIiL6blxe2KukQygQXmkgIiIiIiKFmDQQEREREZFCTBqIiIiIiEghJg1ERERERKQQkwYiIiIiIlKISQMRERERESnEpIGIiIiIiBRi0kBERERERAoxaSDKRaNGjTB69OhiaSs+Ph6CICAyMrJY2vtUvr6+aNeuXYnsm4iIiL4PTBqoVPL19YUgCDmWuLi4kg5NTkREBDp37gxDQ0OoqanB0tISAwYMwK1bt0o6NCIiIipFmDRQqeXp6YnExES5pWrVqiUdluTw4cOoW7cu0tPTsX37dsTExGDbtm3Q1dXF1KlTSzo8IiIiKkWYNFCpJZPJYGRkJLcoKSnlWnfr1q1wdXWFtrY2jIyM0L17dzx58kQqf/HiBXx8fGBgYAB1dXVYWlpi06ZNcm3cvXsXjRs3hoaGBpycnHD+/Pk8Y3v16hX69OmDli1b4tChQ2jWrBmqVq2KOnXqYNGiRVi7di0AIDMzE/369UPVqlWhrq4Oa2trLFu2LNc2Z86cCQMDA+jo6GDw4MF4+/btpx4yIiIiKqWUSzoAom9BRkYGZs+eDWtrazx58gRjxoyBr68vjhw5AgCYOnUqoqOjcfToUZQvXx5xcXF4/fq1XBuTJ0/GokWLYGlpicmTJ8Pb2xtxcXFQVs753/D48eP477//MH78+Fzj0dPTAwBkZWWhcuXK2Lt3L/T19XHu3DkMHDgQxsbG6NKli1Q/KCgIampqCA4ORnx8PPr06QN9fX3MmTMn1/bT09ORnp4uvU5JSfmk40VERETfFyYNVGodPnwYWlpa0usWLVpg7969udbt27ev9LO5uTmWL1+OWrVqITU1FVpaWkhISICzszNcXV0BAGZmZjna8PPzQ6tWrQC8/9bfzs4OcXFxsLGxyVH39u3bAJBr2YdUVFQwc+ZM6XXVqlVx/vx57NmzRy5pUFVVxcaNG6GhoQE7OzvMmjUL48aNw+zZs1GmTM4LjvPmzZNrl4iIiEo3Dk+iUqtx48aIjIyUluXLl+dZ9/Lly/Dy8oKpqSm0tbXh7u4OAEhISAAADBkyBLt27UKNGjUwfvx4nDt3Lkcbjo6O0s/GxsYAIDfE6UOiKBa4H6tWrULNmjVhYGAALS0trFu3Toorm5OTEzQ0NKTX9erVQ2pqKh48eJBrm5MmTUJycrK05FWPiIiISgcmDVRqaWpqwsLCQlqyP8h/LC0tDR4eHtDR0cH27dsRFhaG/fv3A4A0L6BFixa4f/8+fvrpJzx69AhNmzaFn5+fXDsqKirSz4IgAHg/vCg3VlZWAICbN28q7MOuXbvg5+eHfv364a+//kJkZCT69OlT5PkKMpkMOjo6cgsRERGVXkwaiPJx8+ZNPHv2DP7+/mjYsCFsbGxyvUJgYGCA3r17Y9u2bVi6dCnWrVtX6H3++OOPKF++PBYsWJBreVJSEgAgNDQU9evXx9ChQ+Hs7AwLCwvcuXMnR/2oqCi5ORYXLlyAlpYWTExMCh0jERERlR5MGojyYWpqClVVVaxYsQJ3797FoUOHMHv2bLk606ZNw8GDBxEXF4cbN27g8OHDsLW1LfQ+NTU18dtvv+HPP/9EmzZtcPLkScTHxyM8PBzjx4/H4MGDAQCWlpYIDw/H8ePHcevWLUydOhVhYWE52nv79i369euH6OhoHDlyBNOnT8fw4cNznc9ARERE9DF+YiDKh4GBAQICArB3715Ur14d/v7+WLRokVwdVVVVTJo0CY6OjnBzc4OSkhJ27dpVpP22bdsW586dg4qKCrp37w4bGxt4e3sjOTkZP//8MwBg0KBB6NChA7p27Yo6derg2bNnGDp0aI62mjZtCktLS7i5uaFr165o06YNZsyYUaT4iIiIqPQQxE+ZcUlEpVJKSgp0dXXhNGINlGTqJR0OERHRd+Pywl6fre3s9+/k5OQiz0/klQYiIiIiIlKISQMRERERESnEpIGIiIiIiBRi0kBERERERAoxaSAiIiIiIoWYNBARERERkUJMGoiIiIiISCEmDUREREREpJBySQdARN+Ov3/2LvLDYYiIiOjbwysNRERERESkEJMGIiIiIiJSiEkDEREREREpxKSBiIiIiIgUYtJAREREREQKMWkgIiIiIiKFmDQQEREREZFCfE4DERXYA/+60FZTKukwiOgbZzrtWkmHQESfiFcaiIiIiIhIISYNRERERESkEJMGIiIiIiJSiEkDEREREREpxKSBiIiIiIgUYtJAREREREQKMWkgIiIiIiKFmDQQEREREZFCTBpKAV9fX7Rr167I7Rw4cAAWFhZQUlLC6NGjc10XEBAAPT09aZsZM2agRo0axR5LcbXzNTMzM8PSpUtLOgwiIiIiPhH6ayAIgsLy6dOnY8aMGYVuf9myZRBFsUB1bWxscO/ePdy/fx9GRkZyZYMGDUKfPn0wcuRIaGtr57pOWVkZLVu2LJZYACA+Ph5Vq1ZFRESEXPLxqe0U1pkzZzBz5kxERkbizZs3qFSpEurXr4/169dDVVX1s+47LCwMmpqan3UfRERERAXBpOErkJiYKP28e/duTJs2DbGxsdI6LS2tIrWvq6tboHohISF4/fo1OnXqhM2bN2PChAlSWWpqKp48eQIPDw9UrFgxz3UAoK6uXuRY8lNc7SgSHR0NT09PjBgxAsuXL4e6ujpu376Nffv2ITMzs9Dtvn37tkAJh4GBQaH3QURERFScODzpK2BkZCQturq6EARBep2WlgYfHx8YGhpCS0sLtWrVwsmTJ6Vtb968CQ0NDezYsUNat2fPHqirqyM6OhpAwYfybNiwAd27d0fPnj2xceNGaX1wcLB0ZaFJkyYQBCHPdR8PT/rYx7EcO3YMP/zwA/T09KCvr4/WrVvjzp07UnnVqlUBAM7OzhAEAY0aNcq1nfT0dIwcORIVKlSAmpoafvjhB4SFhcn1QRAEBAUFwdXVFRoaGqhfv75ccvaxv/76C0ZGRliwYAHs7e1RrVo1eHp6Yv369XKJUUhICBo2bAh1dXWYmJhg5MiRSEtLk8rNzMwwe/Zs9OrVCzo6Ohg4cCDq168vl5QBwNOnT6GiooK///5b2u7D4UlJSUkYNGgQDA0NoaamBnt7exw+fLjAcfz666+wtLSEmpoaDA0N0alTpzz7TkRERPQhJg1fudTUVLRs2RJBQUGIiIiAp6cnvLy8kJCQAOD9cKJFixZh6NChSEhIwMOHDzF48GDMnz8f1atXL/B+Xr58ib1796JHjx5o3rw5kpOTcfbsWQCQ+3C9b98+JCYm5rnuU6WlpWHMmDEIDw9HUFAQypQpg/bt2yMrKwsAcOnSJQDAyZMnkZiYiMDAwFzbGT9+PPbt24fNmzfjypUrsLCwgIeHB54/fy5Xb/LkyVi8eDHCw8OhrKyMvn375hmbkZEREhMTpQ/xublz5w48PT3RsWNHXL16Fbt370ZISAiGDx8uV2/RokVwcnJCREQEpk6dCh8fH+zatUtuiNXu3btRsWJFNGzYMMd+srKy0KJFC4SGhmLbtm2Ijo6Gv78/lJSUChRHeHg4Ro4ciVmzZiE2NhbHjh2Dm5tbnv1KT09HSkqK3EJERESlF4cnfeWcnJzg5OQkvZ49ezb279+PQ4cOSR8Ihw4diiNHjqBHjx5QVVVFrVq1MGLEiE/az65du2BpaQk7OzsAQLdu3bBhwwY0bNgQqqqqqFChAgCgXLly0lyH3NZ9qo4dO8q93rhxIwwMDBAdHQ17e3tpiI6+vn6e+0hLS8Pq1asREBCAFi1aAADWr1+PEydOYMOGDRg3bpxUd86cOXB3dwcATJw4Ea1atcKbN2+gpqaWo93OnTvj+PHjcHd3h5GREerWrYumTZtKVwwAYN68efDx8ZEmhltaWmL58uVwd3fH6tWrpXabNGmCsWPHSm136dIFo0ePlq4OAMCOHTvg7e2d6xyXkydP4tKlS4iJiYGVlRUAwNzcXCrPL46EhARoamqidevW0NbWRpUqVeDs7Jzr8cxub+bMmXmWExERUenCKw1fudTUVPj5+cHW1hZ6enrQ0tJCTEyMdKUh28aNG3H16lVcuXIFAQEB+U6u/tjGjRvRo0cP6XWPHj2wd+9evHz5slj6kZfbt2/D29sb5ubm0NHRgZmZGQDk6J8id+7cQUZGBho0aCCtU1FRQe3atRETEyNX19HRUfrZ2NgYAPDkyZNc21VSUsKmTZvw8OFDLFiwAJUqVcLcuXNhZ2cnzUOJiopCQEAAtLS0pMXDwwNZWVm4d++e1Jarq6tc2wYGBvjxxx+xfft2AMC9e/dw/vx5+Pj45BpLZGQkKleuLCUMH8svjubNm6NKlSowNzdHz549sX37drx69SrXtgBg0qRJSE5OlpYHDx7kWZeIiIi+f0wavnJ+fn7Yv38/5s6di7NnzyIyMhIODg54+/atXL2oqCikpaUhLS1NbmJ1QURHR+PChQsYP348lJWVoaysjLp16+LVq1fYtWtXcXYnBy8vLzx//hzr16/HxYsXcfHiRQDI0b/ioqKiIv2cnVhlD4XKS6VKldCzZ0+sXLkSN27cwJs3b7BmzRoA75O6QYMGITIyUlqioqJw+/ZtVKtWTWojt7sg+fj44Pfff0dGRgZ27NgBBwcHODg45BqDosnlBYlDW1sbV65cwc6dO2FsbIxp06bByckJSUlJubYnk8mgo6MjtxAREVHpxeFJX7nQ0FD4+vqiffv2AN5/OIyPj5er8/z5c/j6+mLy5MlITEyEj48Prly5ku8HzWwbNmyAm5sbVq1aJbd+06ZN2LBhAwYMGFAsffnYs2fPEBsbi/Xr10tDdEJCQuTqZN9lSNHdiqpVqwZVVVWEhoaiSpUqAICMjAyEhYVJw3WKS9myZWFsbCxNMHZxcUF0dDQsLCw+ua22bdti4MCBOHbsGHbs2IFevXrlWdfR0REPHz7ErVu3cr3aUJA4lJWV0axZMzRr1gzTp0+Hnp4eTp06hQ4dOnxy7ERERFS6MGn4yllaWiIwMBBeXl4QBAFTp07N8c344MGDYWJigilTpiA9PR3Ozs7w8/PLkQTkJiMjA1u3bsWsWbNgb28vV9a/f38sWbIEN27cQKVKlYq1X8D7D+D6+vpYt24djI2NkZCQgIkTJ8rVqVChAtTV1XHs2DFUrlwZampqOW63qqmpiSFDhmDcuHEoV64cTE1NsWDBArx69Qr9+vUrdHxr165FZGQk2rdvj2rVquHNmzfYsmULbty4gRUrVgAAJkyYgLp162L48OHo378/NDU1ER0djRMnTmDlypUK29fU1ES7du0wdepUxMTEwNvbO8+67u7ucHNzQ8eOHbFkyRJYWFjg5s2bEAQBnp6e+cZx+PBh3L17F25ubihbtiyOHDmCrKwsWFtbF/r4EBERUenB4UlfuSVLlqBs2bKoX78+vLy84OHhARcXF6l8y5YtOHLkCLZu3QplZWVoampi27ZtWL9+PY4ePZpv+4cOHcKzZ8+kKxkfsrW1ha2tLTZs2FCsfcpWpkwZ7Nq1C5cvX4a9vT1++uknLFy4UK6OsrIyli9fjrVr16JixYpo27Ztrm35+/ujY8eO6NmzJ1xcXBAXF4fjx4+jbNmyhY6vdu3aSE1NxeDBg2FnZwd3d3dcuHABBw4ckCZTOzo64syZM7h16xYaNmwIZ2dnTJs2Te65FYr4+PggKioKDRs2hKmpqcK6+/btQ61ateDt7Y3q1atj/Pjx0hWY/OLQ09NDYGAgmjRpAltbW6xZswY7d+6UJr4TERERKSKIX+KxukT0TUtJSYGuri6uT7KFtppSSYdDRN8402nXSjoEolIh+/07OTm5yPMTeaWBiIiIiIgUYtJAREREREQKMWkgIiIiIiKFmDQQEREREZFCTBqIiIiIiEghJg1ERERERKQQkwYiIiIiIlKISQMRERERESmkXNIBENG3w2TihSI/HIaIiIi+PbzSQERERERECjFpICIiIiIihZg0EBERERGRQkwaiIiIiIhIISYNRERERESkEJMGIiIiIiJSiEkDEREREREpxOc0EFGBNV/THMrq/LNB9L0KHRFa0iEQ0VeKVxqIiIiIiEghJg1ERERERKQQkwYiIiIiIlKISQMRERERESnEpIGIiIiIiBRi0kBERERERAoxaSAiIiIiIoWYNBARERERkUJMGuirFRwcDEEQkJSUVOBtGjVqhNGjR0uvzczMsHTpUum1IAg4cOBAscX4Nfr4GBAREREVFZMGKhRfX18IgoDBgwfnKBs2bBgEQYCvr++XDywfiYmJaNGixWfdR0BAAPT09D7rPhQJDAzE7NmzS2z/RERE9P1h0kCFZmJigl27duH169fSujdv3mDHjh0wNTUtwcjyZmRkBJlMVtJhfFblypWDtrZ2SYdBRERE3xEmDVRoLi4uMDExQWBgoLQuMDAQpqamcHZ2lqublZWFefPmoWrVqlBXV4eTkxN+//13uTpHjhyBlZUV1NXV0bhxY8THx8uVP3v2DN7e3qhUqRI0NDTg4OCAnTt3flLMHw9PunbtGpo0aQJ1dXXo6+tj4MCBSE1Nlcp9fX3Rrl07LFq0CMbGxtDX18ewYcOQkZHxSfv9UFJSEvr37w8DAwPo6OigSZMmiIqKksrv3LmDtm3bwtDQEFpaWqhVqxZOnjwp18avv/4KS0tLqKmpwdDQEJ06dZLKchuiNXfuXPTt2xfa2towNTXFunXrCh0/ERERlT5MGqhI+vbti02bNkmvN27ciD59+uSoN2/ePGzZsgVr1qzBjRs38NNPP6FHjx44c+YMAODBgwfo0KEDvLy8EBkZif79+2PixIlybbx58wY1a9bEn3/+ievXr2PgwIHo2bMnLl26VKjY09LS4OHhgbJlyyIsLAx79+7FyZMnMXz4cLl6p0+fxp07d3D69Gls3rwZAQEBCAgIKNQ+AaBz58548uQJjh49isuXL8PFxQVNmzbF8+fPAQCpqalo2bIlgoKCEBERAU9PT3h5eSEhIQEAEB4ejpEjR2LWrFmIjY3FsWPH4ObmpnCfixcvhqurKyIiIjB06FAMGTIEsbGxedZPT09HSkqK3EJERESlF5MGKpIePXogJCQE9+/fx/379xEaGooePXrI1UlPT8fcuXOxceNGeHh4wNzcHL6+vujRowfWrl0LAFi9ejWqVauGxYsXw9raGj4+PjnmRFSqVAl+fn6oUaMGzM3NMWLECHh6emLPnj2Fin3Hjh148+YNtmzZAnt7ezRp0gQrV67E1q1b8e+//0r1ypYti5UrV8LGxgatW7dGq1atEBQUVKh9hoSE4NKlS9i7dy9cXV1haWmJRYsWQU9PT7ry4uTkhEGDBsHe3h6WlpaYPXs2qlWrhkOHDgEAEhISoKmpidatW6NKlSpwdnbGyJEjFe63ZcuWGDp0KCwsLDBhwgSUL18ep0+fzrP+vHnzoKurKy0mJiaF6i8RERF9H5RLOgD6thkYGKBVq1YICAiAKIpo1aoVypcvL1cnLi4Or169QvPmzeXWv337VhrGFBMTgzp16siV16tXT+51ZmYm5s6diz179uCff/7B27dvkZ6eDg0NjULFHhMTAycnJ2hqakrrGjRogKysLMTGxsLQ0BAAYGdnByUlJamOsbExrl27Vqh9RkVFITU1Ffr6+nLrX79+jTt37gB4f6VhxowZ+PPPP5GYmIh3797h9evX0pWG5s2bo0qVKjA3N4enpyc8PT3Rvn17hcfB0dFR+lkQBBgZGeHJkyd51p80aRLGjBkjvU5JSWHiQEREVIoxaaAi69u3rzSkZ9WqVTnKs+cI/Pnnn6hUqZJc2adMSl64cCGWLVuGpUuXwsHBAZqamhg9ejTevn1bhOjzp6KiIvdaEARkZWUVqq3U1FQYGxsjODg4R1n2HZf8/Pxw4sQJLFq0CBYWFlBXV0enTp2kfmpra+PKlSsIDg7GX3/9hWnTpmHGjBkICwvL865Nn9oHmUz23U8YJyIiooJj0kBF5unpibdv30IQBHh4eOQor169OmQyGRISEuDu7p5rG7a2ttLwm2wXLlyQex0aGoq2bdtKw5+ysrJw69YtVK9evVBx29raIiAgAGlpadLVhtDQUJQpUwbW1taFajM/Li4uePz4MZSVlWFmZpZrndDQUPj6+qJ9+/YA3icaH08KV1ZWRrNmzdCsWTNMnz4denp6OHXqFDp06PBZ4iYiIqLSjUkDFZmSkhJiYmKknz+mra0NPz8//PTTT8jKysIPP/yA5ORkhIaGQkdHB71798bgwYOxePFijBs3Dv3798fly5dzTDa2tLTE77//jnPnzqFs2bJYsmQJ/v3330InDT4+Ppg+fTp69+6NGTNm4OnTpxgxYgR69uwpDU0qrMzMTERGRsqtk8lkaNasGerVq4d27dphwYIFsLKywqNHj/Dnn3+iffv20jyHwMBAeHl5QRAETJ06Ve6qwOHDh3H37l24ubmhbNmyOHLkCLKysj5bokNERETEpIGKhY6OjsLy2bNnw8DAAPPmzcPdu3ehp6cHFxcX/O9//wMAmJqaYt++ffjpp5+wYsUK1K5dW7pNaLYpU6bg7t278PDwgIaGBgYOHIh27dohOTm5UDFraGjg+PHjGDVqFGrVqgUNDQ107NgRS5YsKVR7H0pNTc1x29lq1aohLi4OR44cweTJk9GnTx88ffoURkZGcHNzkxKVJUuWoG/fvqhfvz7Kly+PCRMmyN29SE9PD4GBgZgxYwbevHkDS0tL7Ny5E3Z2dkWOm4iIiCg3giiKYkkHQURft5SUFOjq6qL2/NpQVud3DUTfq9ARoSUdAhEVo+z37+Tk5Hy/4M0Pb7lKREREREQKMWkgIiIiIiKFmDQQEREREZFCTBqIiIiIiEghJg1ERERERKQQkwYiIiIiIlKISQMRERERESnEpIGIiIiIiBTiU5qIqMBODD5R5IfDEBER0beHVxqIiIiIiEghJg1ERERERKQQkwYiIiIiIlKISQMRERERESnEpIGIiIiIiBRi0kBERERERAoxaSAiIiIiIoX4nAYiKrAQzxbQVOafDaJvifvfZ0o6BCL6DvBKAxERERERKcSkgYiIiIiIFGLSQERERERECjFpICIiIiIihZg0EBERERGRQkwaiIiIiIhIISYNRERERESkEJMGIiIiIiJSiEkDUR4aNWqE0aNHS6/NzMywdOnSEounsAICAqCnp1fSYRAREdE3jEkDfbdEUUSzZs3g4eGRo+zXX3+Fnp4eHj58WAKRFdzjx48xYsQImJubQyaTwcTEBF5eXggKCirp0IiIiKgUYdJA3y1BELBp0yZcvHgRa9euldbfu3cP48ePx4oVK1C5cuUSjFCx+Ph41KxZE6dOncLChQtx7do1HDt2DI0bN8awYcNKOjwiIiIqRZg00HfNxMQEy5Ytg5+fH+7duwdRFNGvXz/8+OOPcHZ2RosWLaClpQVDQ0P07NkT//33X4HbTkhIQNu2baGlpQUdHR106dIF//77LwAgOTkZSkpKCA8PBwBkZWWhXLlyqFu3rrT9tm3bYGJikmf7Q4cOhSAIuHTpEjp27AgrKyvY2dlhzJgxuHDhglRvyZIlcHBwgKamJkxMTDB06FCkpqbmaO/AgQOwtLSEmpoaPDw88ODBgwL3lYiIiEo3Jg303evduzeaNm2Kvn37YuXKlbh+/TrWrl2LJk2awNnZGeHh4Th27Bj+/fdfdOnSpUBtZmVloW3btnj+/DnOnDmDEydO4O7du+jatSsAQFdXFzVq1EBwcDAA4Nq1axAEAREREdIH+jNnzsDd3T3X9p8/f45jx45h2LBh0NTUzFH+4RyFMmXKYPny5bhx4wY2b96MU6dOYfz48XL1X716hTlz5mDLli0IDQ1FUlISunXrlmf/0tPTkZKSIrcQERFR6cWkgUqFdevW4fr16xg9ejTWrVuHtWvXwtnZGXPnzoWNjQ2cnZ2xceNGnD59Grdu3cq3vaCgIFy7dg07duxAzZo1UadOHWzZsgVnzpxBWFgYgPcTqbOThuDgYDRv3hy2trYICQmR1uWVNMTFxUEURdjY2OQby+jRo9G4cWOYmZmhSZMm+Pnnn7Fnzx65OhkZGVi5ciXq1auHmjVrYvPmzTh37hwuXbqUa5vz5s2Drq6utCi6IkJERETfPyYNVCpUqFABgwYNgq2tLdq1a4eoqCicPn0aWlpa0pL9Af3OnTv5thcTEwMTExO5D9PVq1eHnp4eYmJiAADu7u4ICQlBZmYmzpw5g0aNGkmJxKNHjxAXF4dGjRrl2r4oigXu28mTJ9G0aVNUqlQJ2tra6NmzJ549e4ZXr15JdZSVlVGrVi3ptY2NjVysH5s0aRKSk5OlhUOZiIiISjflkg6A6EtRVlaGsvL7X/nU1FR4eXlh/vz5OeoZGxsXy/7c3Nzw8uVLXLlyBX///Tfmzp0LIyMj+Pv7w8nJCRUrVoSlpWWu21paWkIQBNy8eVPhPuLj49G6dWsMGTIEc+bMQbly5RASEoJ+/frh7du30NDQKFTsMpkMMpmsUNsSERHR96fQVxrOnj2LHj16oF69evjnn38AAFu3bpWGXhB9zVxcXHDjxg2YmZnBwsJCbsltDsHHbG1t8eDBA7lv4KOjo5GUlITq1asDeD/vwNHREStXroSKigpsbGzg5uaGiIgIHD58OM+hSQBQrlw5eHh4YNWqVUhLS8tRnpSUBAC4fPkysrKysHjxYtStWxdWVlZ49OhRjvrv3r2TJmUDQGxsLJKSkmBra5tvX4mIiIgKlTTs27cPHh4eUFdXR0REBNLT0wG8v2PM3LlzizVAos9h2LBheP78Oby9vREWFoY7d+7g+PHj6NOnDzIzM/PdvlmzZnBwcICPjw+uXLmCS5cuoVevXnB3d4erq6tUr1GjRti+fbuUIJQrVw62trbYvXu3wqQBAFatWoXMzEzUrl0b+/btw+3btxETE4Ply5ejXr16AAALCwtkZGRgxYoVuHv3LrZu3Yo1a9bkaEtFRQUjRozAxYsXcfnyZfj6+qJu3bqoXbv2pxw2IiIiKqUKlTT8/PPPWLNmDdavXw8VFRVpfYMGDXDlypViC47oc6lYsSJCQ0ORmZmJH3/8EQ4ODhg9ejT09PRQpkz+/y0EQcDBgwdRtmxZuLm5oVmzZjA3N8fu3bvl6rm7uyMzM1Nu7kKjRo1yrMuNubk5rly5gsaNG2Ps2LGwt7dH8+bNERQUhNWrVwMAnJycsGTJEsyfPx/29vbYvn075s2bl6MtDQ0NTJgwAd27d0eDBg2gpaWVI1YiIiKivAjip8y4/P80NDQQHR0NMzMzaGtrIyoqCubm5rh79y6qV6+ON2/efI5YiaiEpKSkQFdXF3/Wqw9NZU6FIvqWuP99pqRDIKISkv3+nZycDB0dnSK1VagrDUZGRoiLi8uxPiQkBObm5kUKiIiIiIiIvi6FShoGDBiAUaNG4eLFixAEAY8ePcL27dvh5+eHIUOGFHeMRERERERUggo1zmDixInIyspC06ZN8erVK7i5uUEmk8HPzw8jRowo7hiJiIiIiKgEFWpOQ7a3b98iLi4OqampqF69OrS0tIozNiL6SnBOA9G3i3MaiEqv4pzTUKR3f1VVVeme9ERERERE9H0qVNLw5s0brFixAqdPn8aTJ0+QlZUlV87brhIRERERfT8KlTT069cPf/31Fzp16oTatWtDEITijouIiIiIiL4ShUoaDh8+jCNHjqBBgwbFHQ8REREREX1lCpU0VKpUCdra2sUdCxF95X44drTIE6mIiIjo21Oo5zQsXrwYEyZMwP3794s7HiIiIiIi+soU6kqDq6sr3rx5A3Nzc2hoaEBFRUWu/Pnz58USHBERERERlbxCJQ3e3t74559/MHfuXBgaGnIiNBERERHRd6xQScO5c+dw/vx5ODk5FXc8RERERET0lSnUnAYbGxu8fv26uGMhIiIiIqKvUKGSBn9/f4wdOxbBwcF49uwZUlJS5BYiIiIiIvp+CKIoip+6UZky73ONj+cyiKIIQRCQmZlZPNER0VchJSUFurq6SE5O5i1XiYiIvhHF+f5dqDkNp0+fLtJOiejbtPZ/R6Eu0yjpMIhKneGLvUo6BCIq5QqVNLi7uxd3HERERERE9JUqVNKQ7dWrV0hISMDbt2/l1js6OhYpKCIiIiIi+noUKml4+vQp+vTpg6NHj+ZazjkNRERERETfj0LdPWn06NFISkrCxYsXoa6ujmPHjmHz5s2wtLTEoUOHijtGIiIiIiIqQYW60nDq1CkcPHgQrq6uKFOmDKpUqYLmzZtDR0cH8+bNQ6tWrYo7TiIiIiIiKiGFutKQlpaGChUqAADKli2Lp0+fAgAcHBxw5cqV4ouOiIiIiIhKXKGSBmtra8TGxgIAnJycsHbtWvzzzz9Ys2YNjI2NizVAIiIiIiIqWYUanjRq1CgkJiYCAKZPnw5PT09s374dqqqqCAgIKM74iIiIiIiohBXqSkOPHj3g6+sLAKhZsybu37+PsLAwPHjwAF27di3O+L4Jvr6+aNeuXZHbOXDgACwsLKCkpITRo0fnui4gIAB6enrSNjNmzECNGjWKPZbiaudrZmZmhqVLl5Z0GLn6XOeViIiIqDAKlTR8TENDAy4uLihfvnxxNFfsBEFQuMyYMaNI7S9btqzAV1hsbGwgk8nw+PHjHGWDBg1Cp06d8ODBA8yePTvXdV27dsWtW7eKJRYAiI+PhyAIiIyMLFI7hXXmzBk0adIE5cqVg4aGBiwtLdG7d+8cz/74HMLCwjBw4MDP0vbs2bNhbGyM58+fy62PioqCTCbD4cOHP8t+iYiIiD6HQg1PyszMREBAAIKCgvDkyRNkZWXJlZ86dapYgisu2UOpAGD37t2YNm2aNCcDALS0tIrUvq6uboHqhYSE4PXr1+jUqRM2b96MCRMmSGWpqal48uQJPDw8ULFixTzXAYC6unqRY8lPcbWjSHR0NDw9PTFixAgsX74c6urquH37Nvbt21ekZ328ffsWqqqq+dYzMDAo9D7yM2nSJPzxxx8YNmwYdu7cCQDIyMhA79690aNHD7Ru3fqz7ZuIiIiouBXqSsOoUaMwatQoZGZmwt7eHk5OTnLL18bIyEhadHV1IQiC9DotLQ0+Pj4wNDSElpYWatWqhZMnT0rb3rx5ExoaGtixY4e0bs+ePVBXV0d0dDSAgg8d2bBhA7p3746ePXti48aN0vrg4GBoa2sDAJo0aQJBEPJc9/HwpI99HMuxY8fwww8/QE9PD/r6+mjdujXu3LkjlVetWhUA4OzsDEEQ0KhRo1zbSU9Px8iRI1GhQgWoqanhhx9+QFhYmFwfBEFAUFAQXF1doaGhgfr168slZx/766+/YGRkhAULFsDe3h7VqlWDp6cn1q9fL5cYhYSEoGHDhlBXV4eJiQlGjhyJtLQ0qdzMzAyzZ89Gr169oKOjg4EDB6J+/fpySRnw/qGEKioq+Pvvv6XtPhyelJSUhEGDBsHQ0BBqamqwt7eXuyKQXxwfUlZWxpYtW3DgwAH8/vvvAIA5c+YgKSkJv/zyC5KSktC/f38YGBhAR0cHTZo0QVRUVJ7H6mP5nQ9XV1csWrRIet2uXTuoqKggNTUVAPDw4UMIgoC4uLgC75OIiIhKr0IlDbt27cKePXuwe/duLF26FL/88ovc8i1JTU1Fy5YtERQUhIiICHh6esLLywsJCQkA3g8nWrRoEYYOHYqEhAQ8fPgQgwcPxvz581G9evUC7+fly5fYu3cvevTogebNmyM5ORlnz54FALkP1/v27UNiYmKe6z5VWloaxowZg/DwcAQFBaFMmTJo3769dHXo0qVLAICTJ08iMTERgYGBubYzfvx47Nu3D5s3b8aVK1dgYWEBDw+PHMNvJk+ejMWLFyM8PBzKysro27dvnrEZGRkhMTFR+hCfmzt37sDT0xMdO3bE1atXsXv3boSEhGD48OFy9RYtWgQnJydERERg6tSp8PHxwa5duyCKolRn9+7dqFixIho2bJhjP1lZWWjRogVCQ0Oxbds2REdHw9/fH0pKSp8Ux4dsbGwwb948DBkyBMePH8e8efOwadMm6OjooHPnznjy5AmOHj2Ky5cvw8XFBU2bNs1xPPOS3/lwd3dHcHAwAEAURZw9exZ6enoICQkB8H5YWKVKlWBhYZFr++np6UhJSZFbiIiIqPQqVNKgqqqa54eNb42TkxMGDRoEe3t7WFpaYvbs2ahWrZrck62HDh2KH374QZoAXqtWLYwYMeKT9rNr1y5YWlrCzs4OSkpK6NatGzZs2ADg/fHMfu5FuXLlYGRklOe6T9WxY0d06NABFhYWqFGjBjZu3Ihr165JV0myh+jo6+vDyMgI5cqVy9FGWloaVq9ejYULF6JFixaoXr26dDUguw/Z5syZA3d3d1SvXh0TJ07EuXPn8ObNm1xj69y5M7y9veHu7g5jY2O0b98eK1eulPuAOm/ePPj4+GD06NGwtLRE/fr1sXz5cmzZskWu3SZNmmDs2LGoVq0aqlWrhi5duuDRo0fSh2QA2LFjB7y9vSEIQo5YTp48iUuXLiEwMBDNmzeHubk5WrdujRYtWnxSHB8bNWoU7O3t0bJlSwwZMgSNGzdGSEgILl26hL1798LV1RWWlpZYtGgR9PT0pKsSihTkfDRq1AghISHIzMzE1atXoaqqCh8fHymRCA4Ohru7e577mDdvHnR1daXFxMQk37iIiIjo+1WopGHs2LFYtmyZ3Le436rU1FT4+fnB1tYWenp60NLSQkxMjHSlIdvGjRtx9epVXLlyBQEBAbl+8FRk48aN6NGjh/S6R48e2Lt3L16+fFks/cjL7du34e3tDXNzc+jo6MDMzAwAcvRPkTt37iAjIwMNGjSQ1qmoqKB27dqIiYmRq+vo6Cj9nP3MjidPnuTarpKSEjZt2oSHDx9iwYIFqFSpEubOnQs7OztpHkpUVBQCAgKgpaUlLR4eHsjKysK9e/ektlxdXeXaNjAwwI8//ojt27cDAO7du4fz58/Dx8cn11giIyNRuXJlWFlZ5Vpe0Dg+JggCJk+ejKysLEyZMkVqKzU1Ffr6+nLt3bt3T27oWF4Kcj4aNmyIly9fIiIiAmfOnIG7uzsaNWokJQ1nzpyRhqLlZtKkSUhOTpaWBw8e5BsXERERfb8KNRE6JCQEp0+fxtGjR2FnZwcVFRW58ryGuHyN/Pz8cOLECSxatAgWFhZQV1dHp06dcty9JyoqCmlpaShTpgwSExM/6SF20dHRuHDhAi5duiQ3zj4zMxO7du3CgAEDiq0/H/Py8kKVKlWwfv16VKxYEVlZWbC3t/9sdyf68HchO7H6eKL8xypVqoSePXuiZ8+emD17NqysrLBmzRrMnDkTqampGDRoEEaOHJljO1NTU+lnTU3NHOU+Pj4YOXIkVqxYgR07dsDBwQEODg65xqBocjmAAseRG2VlZbl/U1NTYWxsLH2A/5Ci+SqfQk9PD05OTggODsb58+fRvHlzuLm5SXffun37tsIrDTKZDDKZrFhiISIiom9foZIGPT09tG/fvrhjKRGhoaHw9fWV+pOamor4+Hi5Os+fP4evry8mT56MxMRE+Pj44MqVK/l+0My2YcMGuLm5YdWqVXLrN23ahA0bNny2pOHZs2eIjY3F+vXrpXH8Hw7XASANeVJ0t6Jq1apBVVUVoaGhqFKlCoD3dwIKCwuTnidRXMqWLQtjY2NpgrGLiwuio6MLNRyubdu2GDhwII4dO4YdO3agV69eedZ1dHTEw4cPcevWrVyvNhQljtzaevz4MZSVlaUrP5+ioOfD3d0dp0+fxqVLlzBnzhyUK1cOtra2mDNnDoyNjfO8qkJERET0sUIlDZs2bSpQvdDQULi6un7V31haWloiMDAQXl5eEAQBU6dOzfHN+ODBg2FiYoIpU6YgPT0dzs7O8PPzy5EE5CYjIwNbt27FrFmzYG9vL1fWv39/LFmyBDdu3EClSpWKtV/A+w/g+vr6WLduHYyNjZGQkICJEyfK1alQoQLU1dVx7NgxVK5cGWpqajlut6qpqYkhQ4Zg3LhxKFeuHExNTbFgwQK8evUK/fr1K3R8a9euRWRkJNq3b49q1arhzZs32LJlC27cuIEVK1YAACZMmIC6deti+PDh6N+/PzQ1NREdHY0TJ05g5cqVCtvX1NREu3btMHXqVMTExMDb2zvPuu7u7nBzc0PHjh2xZMkSWFhY4ObNmxAEAZ6enkWK42PNmjVDvXr10K5dOyxYsABWVlZ49OgR/vzzT7Rv3z7HUKvc+lWQ89GoUSOsWLECBgYGsLGxkdatXLkSnTt3/qSYiYiIqHQrloe75aVFixb4559/PucuimzJkiUoW7Ys6tevDy8vL3h4eMDFxUUq37JlC44cOYKtW7dCWVkZmpqa2LZtG9avX4+jR4/m2/6hQ4fw7NmzXK/M2NrawtbWNsdk4uJSpkwZ7Nq1C5cvX4a9vT1++uknLFy4UK6OsrIyli9fjrVr16JixYpo27Ztrm35+/ujY8eO6NmzJ1xcXBAXF4fjx4+jbNmyhY6vdu3aSE1NxeDBg2FnZwd3d3dcuHABBw4ckIbOODo64syZM7h16xYaNmwIZ2dnTJs2Te65FYr4+PggKioKDRs2zHcY0b59+1CrVi14e3ujevXqGD9+vHQFpqhxfEgQBBw5cgRubm7o06cPrKys0K1bN9y/fx+GhoYFaqMg56Nhw4bIysqSG4bUqFEjZGZmKpzPQERERPQxQfyMs5m1tbURFRUFc3Pzz7ULIvoCUlJSoKuriwXDdkFdplHS4RCVOsMXe5V0CET0Dcp+/05OToaOjk6R2vqsVxqIiIiIiOjbx6SBiIiIiIgUYtJAREREREQKfdak4VMfgEZERERERF+fz5o0fA9PjCYiIiIiKu0KlTRMnz4d9+/fz7fey5cveeckIiIiIqJvXKGShoMHD6JatWpo2rQpduzYgfT09OKOi4iIiIiIvhKFShoiIyMRFhYGOzs7jBo1CkZGRhgyZAjCwsKKOz4iIiIiIiphRX64W0ZGBv744w9s2rQJx48fh42NDfr16wdfX1/o6uoWV5xEVIKK8+EwRERE9GV8VQ93E0URGRkZePv2LURRRNmyZbFy5UqYmJhg9+7dRW2eiIiIiIhKWKGThsuXL2P48OEwNjbGTz/9BGdnZ8TExODMmTO4ffs25syZg5EjRxZnrEREREREVAIKNTzJwcEBN2/exI8//ogBAwbAy8sLSkpKcnX+++8/VKhQAVlZWcUWLBGVDA5PIiIi+vYU5/u3cmE26tKlC/r27YtKlSrlWad8+fJMGIiIiIiIvgNFnghNRN8/XmkgIiL69pT4lYbMzEwEBAQgKCgIT548yXFF4dSpU0UKioiIiIiIvh6FShpGjRqFgIAAtGrVCvb29hAEobjjIqKv0MIBPaGmolLSYRCVGpO3/V7SIRARAShk0rBr1y7s2bMHLVu2LO54iIiIiIjoK1OoW66qqqrCwsKiuGMhIiIiIqKvUKGShrFjx2LZsmXgHGoiIiIiou9foYYnhYSE4PTp0zh69Cjs7Oyg8tEY58DAwGIJjoiIiIiISl6hkgY9PT20b9++uGMhIiIiIqKvUKGShk2bNhV3HERERERE9JUq1JwGIiIiIiIqPQp1pQEAfv/9d+zZswcJCQl4+/atXNmVK1eKHBgREREREX0dCnWlYfny5ejTpw8MDQ0RERGB2rVrQ19fH3fv3kWLFi2KO0YiIiIiIipBhUoafv31V6xbtw4rVqyAqqoqxo8fjxMnTmDkyJFITk4u7hi/WoIg4MCBA3mWBwcHQxAEJCUlfbGYviWNGjXC6NGjSzqMYhMfHw9BEBAZGVnSoRAREREVq0IlDQkJCahfvz4AQF1dHS9fvgQA9OzZEzt37iy+6ErQ48ePMWLECJibm0Mmk8HExAReXl4ICgoqcBv169dHYmIidHV1P2OkxUMURaxbtw516tSBlpYW9PT04OrqiqVLl+LVq1efZZ+BgYGYPXv2Z2n7U719+xYLFiyAk5MTNDQ0UL58eTRo0ACbNm1CRkbGZ99/XFwc+vTpg8qVK0Mmk6Fq1arw9vZGeHj4Z983ERERUX4KlTQYGRnh+fPnAABTU1NcuHABAHDv3r3v4oFv8fHxqFmzJk6dOoWFCxfi2rVrOHbsGBo3boxhw4YVuB1VVVUYGRlBEITPGG3x6NmzJ0aPHo22bdvi9OnTiIyMxNSpU3Hw4EH89ddfhW43tw/c2XNgypUrB21t7UK3XVzevn0LDw8P+Pv7Y+DAgTh37hwuXbqEYcOGYcWKFbhx48Zn3X94eDhq1qyJW7duYe3atYiOjsb+/fthY2ODsWPHFrrdj+caERERERVWoZKGJk2a4NChQwCAPn364KeffkLz5s3RtWvX7+L5DUOHDoUgCLh06RI6duwIKysr2NnZYcyYMVKClO2///5D+/btoaGhAUtLS+m4ALkPTwoNDUWjRo2goaGBsmXLwsPDAy9evAAAHDt2DD/88AP09PSgr6+P1q1b486dO3L7O3fuHGrUqAE1NTW4urriwIEDOYbEnDlzBrVr14ZMJoOxsTEmTpyId+/e5dnfPXv2YPv27di5cyf+97//oVatWjAzM0Pbtm1x6tQpNG7cGAAQFhaG5s2bo3z58tDV1YW7u3uOSe+CIGD16tVo06YNNDU1MWfOHMyYMQM1atTAb7/9hqpVq0JNTQ1AzuFJW7duhaurK7S1tWFkZITu3bvjyZMncu0fOnQIlpaWUFNTQ+PGjbF58+YcxzgkJAQNGzaEuro6TExMMHLkSKSlpeXZ/6VLl+Lvv/9GUFAQhg0bhho1asDc3Bzdu3fHxYsXYWlpWeDzAwA3b95E/fr1oaamBnt7e5w5cybPfYuiCF9fX1haWuLs2bNo1aoVqlWrhho1amD69Ok4ePCgVPfatWto0qQJ1NXVoa+vj4EDByI1NVUq9/X1Rbt27TBnzhxUrFgR1tbWAAAzMzPMnj0b3t7e0NTURKVKlbBq1ao8YyIiIiL6WKGShnXr1mHy5MkAgGHDhmHjxo2wtbXFrFmzsHr16mIN8Et7/vw5jh07hmHDhkFTUzNHuZ6entzrmTNnokuXLrh69SpatmwJHx8f6SrMxyIjI9G0aVNUr14d58+fR0hICLy8vJCZmQkASEtLw5gxYxAeHo6goCCUKVMG7du3R1ZWFgAgJSUFXl5ecHBwwJUrVzB79mxMmDBBbh///PMPWrZsiVq1aiEqKgqrV6/Ghg0b8PPPP+fZ5+3bt8Pa2hpt27bNUSYIgjS86uXLl+jduzdCQkJw4cIFWFpaomXLltLwtGwzZsxA+/btce3aNfTt2xfA++E3+/btQ2BgYJ5j/jMyMjB79mxERUXhwIEDiI+Ph6+vr1R+7949dOrUCe3atUNUVBQGDRok/R5mu3PnDjw9PdGxY0dcvXoVu3fvRkhICIYPH66w/82aNYOzs3OOMhUVFen3IL/zk23cuHEYO3YsIiIiUK9ePXh5eeHZs2e57jsyMhI3btzA2LFjUaZMzv+O2b9vaWlp8PDwQNmyZREWFoa9e/fi5MmTOfoVFBSE2NhYnDhxAocPH5bWL1y4EE5OToiIiMDEiRMxatQonDhxIs9jkp6ejpSUFLmFiIiISq9C3XK1TJkych9wunXrhm7duhVbUCUpLi4OoijCxsamQPV9fX3h7e0NAJg7dy6WL1+OS5cuwdPTM0fdBQsWwNXVFb/++qu0zs7OTvq5Y8eOcvU3btwIAwMDREdHw97eHjt27IAgCFi/fj3U1NRQvXp1/PPPPxgwYIC0za+//goTExOsXLkSgiDAxsYGjx49woQJEzBt2rRcP5jevn1b+lZakSZNmsi9XrduHfT09HDmzBm0bt1aWt+9e3f06dNHru7bt2+xZcsWGBgY5Nl+doIBAObm5li+fDlq1aqF1NRUaGlpYe3atbC2tsbChQsBANbW1rh+/TrmzJkjbTdv3jz4+PhIVzAsLS2xfPlyuLu7Y/Xq1dJVjo/736hRo3z7n9/5yTZ8+HCp7urVq3Hs2DFs2LAB48ePz3XfAPL9fduxYwfevHmDLVu2SEnMypUr4eXlhfnz58PQ0BAAoKmpid9++w2qqqpy2zdo0AATJ04EAFhZWSE0NBS//PILmjdvnuv+5s2bh5kzZyqMiYiIiEqPQj/c7cWLF1i0aBH69euHfv36YfHixXl+w/4t+dQ5GY6OjtLPmpqa0NHRyTGkJlv2lYa83L59G97e3jA3N4eOjg7MzMwAvJ94DgCxsbFwdHSU++Bbu3ZtuTZiYmJQr149uXkUDRo0QGpqKh4+fJjrfgva53///RcDBgyApaUldHV1oaOjg9TUVCm+bK6urjm2rVKlisKEAQAuX74MLy8vmJqaQltbG+7u7gDk+1+rVi25bT7uf1RUFAICAqClpSUtHh4eyMrKwr1793Ldb0H7n9/5yVavXj3pZ2VlZbi6uiImJqZI+46JiYGTk5Pc1a8GDRogKysLsbGx0joHB4ccCcPHMWW/zismAJg0aRKSk5Ol5cGDBwWKk4iIiL5PhbrS8Pfff6NNmzbQ0dGRPiAuX74cs2bNwh9//AE3N7diDfJLsrS0hCAIuHnzZoHqq6ioyL0WBCHHcJVs6urqCtvy8vJClSpVsH79elSsWBFZWVmwt7f/7BNaraysCtTf3r1749mzZ1i2bBmqVKkCmUyGevXq5Ygvt2Fdua37UPbwGw8PD2zfvh0GBgZISEiAh4fHJ/U/NTUVgwYNwsiRI3OUmZqa5rpNQfv/Oc6PlZUVgPfzIHIbHvWp8jvOBSWTySCTyYqlLSIiIvr2FepKw7Bhw9ClSxfcu3cPgYGBCAwMxN27d9GtW7dPurvQ16hcuXLw8PDAqlWrcp08W5RnLjg6OuZ5y9Znz54hNjYWU6ZMQdOmTWFraytNkM5mbW2Na9euIT09XVoXFhYmV8fW1hbnz5+X+wY7NDQU2traqFy5cq777t69O27duiU36TabKIrSszdCQ0MxcuRItGzZEnZ2dpDJZPjvv/8K1vl83Lx5E8+ePYO/vz8aNmwIGxubHFdsrK2tc9yC9OP+u7i4IDo6GhYWFjmW3L6BB973/+TJk4iIiMhRlpGRgbS0tAKdn2wfTpZ/9+4dLl++DFtb21zr1qhRA9WrV8fixYtzTTazf99sbW0RFRUl9zsZGhqKMmXKFGho2ccT+C9cuJBnTEREREQfK1TSEBcXh7Fjx0JJSUlap6SkhDFjxiAuLq7Ygispq1atQmZmJmrXro19+/bh9u3biImJwfLly3MM8/gUkyZNQlhYGIYOHYqrV6/i5s2bWL16Nf777z+ULVsW+vr6WLduHeLi4nDq1CmMGTNGbvvu3bsjKysLAwcORExMDI4fP45FixYBgDQcaejQoXjw4AFGjBiBmzdv4uDBg5g+fTrGjBmT63wGAOjSpQu6du0Kb29vzJ07F+Hh4bh//z4OHz6MZs2a4fTp0wDeX4XZunUrYmJicPHiRfj4+OR79aSgTE1NoaqqihUrVuDu3bs4dOhQjmc4DBo0CDdv3sSECRNw69Yt7NmzBwEBAXL9nzBhAs6dO4fhw4cjMjISt2/fxsGDBxVOhB49ejQaNGiApk2bYtWqVYiKisLdu3exZ88e1K1bF7dv3y7Q+cm2atUq7N+/Hzdv3sSwYcPw4sULufkaHxIEAZs2bcKtW7fQsGFDHDlyBHfv3sXVq1cxZ84caXK6j48P1NTU0Lt3b1y/fh2nT5/GiBEj0LNnT2k+gyKhoaFYsGABbt26hVWrVmHv3r0YNWpUvtsRERERAYVMGlxcXHIdD5097vpbZ25ujitXrqBx48YYO3Ys7O3t0bx5cwQFBRXp7lBWVlb466+/EBUVhdq1a6NevXo4ePAglJWVUaZMGezatQuXL1+Gvb09fvrpJ2nCbzYdHR388ccfiIyMRI0aNTB58mRMmzYNAKR5DpUqVcKRI0dw6dIlODk5YfDgwejXrx+mTJmSZ1yCIGDHjh1YsmQJDhw4AHd3dzg6OmLGjBlo27YtPDw8AAAbNmzAixcv4OLigp49e2LkyJGoUKFCoY/HhwwMDBAQEIC9e/eievXq8Pf3lxKibFWrVsXvv/+OwMBAODo6YvXq1dLdk7KH0jg6OuLMmTPSh3BnZ2dMmzYNFStWzHPfMpkMJ06cwPjx47F27VrUrVsXtWrVwvLlyzFy5EjY29sX6Pxk8/f3h7+/P5ycnBASEoJDhw6hfPnyee6/du3aCA8Ph4WFBQYMGABbW1u0adMGN27cwNKlSwEAGhoaOH78OJ4/f45atWqhU6dOaNq0KVauXFmg4zt27FiEh4fD2dkZP//8M5YsWSKdVyIiIqL8CGIhnsa2e/dujB8/HiNGjEDdunUBvB/usGrVKvj7+8sNe/hwojAVv+3bt6NPnz5ITk4utm/9vyVz5szBmjVrOFFXATMzM4wePVrumRifKiUlBbq6upjSpQ3UPprHQ0Sfz+Rtv5d0CET0Dct+/05OToaOjk6R2irUROjsW4zmdgtJb29vCIIAURQhCIL0DAIqHlu2bIG5uTkqVaqEqKgoTJgwAV26dCk1CcOvv/6KWrVqQV9fH6GhoVi4cKHCoUdEREREVHSFShryunUlfX6PHz/GtGnT8PjxYxgbG6Nz585yzyn43t2+fRs///wznj9/DlNTU4wdOxaTJk0q6bCIiIiIvmuFGp5ERKULhycRlQwOTyKioijx4UkA8OjRI4SEhODJkyc5bhWZ2z3yiYiIiIjo21SopCEgIACDBg2Cqqoq9PX15Z4+LAgCkwYiIiIiou9IoZKGqVOnYtq0aZg0aVKe9/4nIiIiIqLvQ6E+8b969QrdunVjwkBEREREVAoU6lN/v379sHfv3uKOhYiIiIiIvkKFuntSZmYmWrdujdevX8PBwQEqH91NZcmSJcUWIBGVvOK8+wIRERF9GSV+96R58+bh+PHjsLa2BoAcE6GJiIiIiOj7UaikYfHixdi4cSN8fX2LORwiIiIiIvraFGpOg0wmQ4MGDYo7FiIiIiIi+goVKmkYNWoUVqxYUdyxEBERERHRV6hQw5MuXbqEU6dO4fDhw7Czs8sxETowMLBYgiMiIiIiopJXqKRBT08PHTp0KO5YiIiIiIjoK1SopGHTpk3FHQcRfQNiF56BlppmSYdB9NWzndykpEMgIipWhUoasj19+hSxsbEAAGtraxgYGBRLUERERERE9PUo1ETotLQ09O3bF8bGxnBzc4ObmxsqVqyIfv364dWrV8UdIxERERERlaBCJQ1jxozBmTNn8McffyApKQlJSUk4ePAgzpw5g7FjxxZ3jEREREREVIIKNTxp3759+P3339GoUSNpXcuWLaGuro4uXbpg9erVxRUfERERERGVsEJdaXj16hUMDQ1zrK9QoQKHJxERERERfWcKlTTUq1cP06dPx5s3b6R1r1+/xsyZM1GvXr1iC46IiIiIiEpeoYYnLV26FJ6enqhcuTKcnJwAAFFRUZDJZPjrr7+KNUAiIiIiIipZhUoaHBwccPv2bWzfvh03b94EAHh7e8PHxwfq6urFGiAREREREZWsQiUN8+bNg6GhIQYMGCC3fuPGjXj69CkmTJhQLMEREREREVHJK9SchrVr18LGxibHejs7O6xZs6bIQREVJ0EQcODAgZIOo1g1atQIo0ePLukwiIiIqJQoVNLw+PFjGBsb51hvYGCAxMTEIgdFVFCPHz/GiBEjYG5uDplMBhMTE3h5eSEoKEiqk5iYiBYtWgAA4uPjIQgCIiMji7zvqKgotGnTBhUqVICamhrMzMzQtWtXPHnypMht5ycwMBCzZ8/+7PshIiIiAgo5PMnExAShoaGoWrWq3PrQ0FBUrFixWAIjyk98fDwaNGgAPT09LFy4EA4ODsjIyMDx48cxbNgwab6NkZFRse/76dOnaNq0KVq3bo3jx49DT08P8fHxOHToENLS0grd7tu3b6GqqppvvXLlyhV6H0RERESfqlBXGgYMGIDRo0dj06ZNuH//Pu7fv4+NGzfip59+yjHPgehzGTp0KARBwKVLl9CxY0dYWVnBzs4OY8aMwYULF6R6Hw5Pyk50nZ2dIQgCGjVqhL///hsqKip4/PixXPujR49Gw4YNc913aGgokpOT8dtvv8HZ2RlVq1ZF48aN8csvv8gl09evX0eLFi2gpaUFQ0ND9OzZE//9959U3qhRIwwfPhyjR49G+fLl4eHhge7du6Nr165y+8vIyED58uWxZcsWabsPhyelp6djwoQJMDExgUwmg4WFBTZs2FDgOIiIiIgUKVTSMG7cOPTr1w9Dhw6Fubk5zM3NMWLECIwcORKTJk0q7hiJcnj+/DmOHTuGYcOGQVNTM0e5np5erttdunQJAHDy5EkkJiYiMDAQbm5uMDc3x9atW6V6GRkZ2L59O/r27ZtrO0ZGRnj37h32798PURRzrZOUlIQmTZrA2dkZ4eHhOHbsGP7991906dJFrt7mzZuhqqqK0NBQrFmzBj4+Pvjjjz+Qmpoq1Tl+/DhevXqF9u3b57qvXr16YefOnVi+fDliYmKwdu1aaGlpfVIcH0pPT0dKSorcQkRERKVXoYYnCYKA+fPnY+rUqYiJiYG6ujosLS0hk8mKOz6iXMXFxUEUxVwn5CtiYGAAANDX15cbttSvXz9s2rQJ48aNAwD88ccfePPmTZ4frOvWrYv//e9/6N69OwYPHozatWujSZMm6NWrl/S09JUrV8LZ2Rlz586Vttu4cSNMTExw69YtWFlZAQAsLS2xYMECqU61atWgqamJ/fv3o2fPngCAHTt2oE2bNtDW1s4Ry61bt7Bnzx6cOHECzZo1AwCYm5tL5QWN40Pz5s3DzJkzFR1KIiIiKkUKdaUhm5aWFmrVqgV7e3smDPRF5fXtfmH5+voiLi5OGtYUEBCALl265HoVI9ucOXPw+PFjrFmzRrpzmI2NDa5duwbg/UTp06dPQ0tLS1qyk5w7d+5I7dSsWVOuXWVlZXTp0gXbt28HAKSlpeHgwYPw8fHJNY7IyEgoKSnB3d091/KCxvGhSZMmITk5WVoePHiQ53EgIiKi71+hrjQQlTRLS0sIgiBNdi6qChUqwMvLC5s2bULVqlVx9OhRBAcH57udvr4+OnfujM6dO2Pu3LlwdnbGokWLsHnzZqSmpsLLywvz58/Psd2Hdx/LLTHx8fGBu7s7njx5ghMnTkBdXR2enp65xpDfAxULGseHZDIZvwggIiIiCZMG+iaVK1cOHh4eWLVqFUaOHJnjg3dSUlKu8xqy70yUmZmZo6x///7w9vZG5cqVUa1aNTRo0OCTYlJVVUW1atWkuye5uLhg3759MDMzg7Lyp/1Xq1+/PkxMTLB7924cPXoUnTt3hoqKSq51HRwckJWVhTNnzkjDkz5UlDiIiIiIgCIOTyIqSatWrUJmZiZq166Nffv24fbt24iJicHy5ctRr169XLepUKEC1NXVpcnAycnJUpmHhwd0dHTw888/o0+fPgr3ffjwYfTo0QOHDx/GrVu3EBsbi0WLFuHIkSNo27YtAGDYsGF4/vw5vL29ERYWhjt37uD48ePo06dPrknLx7p37441a9bgxIkTeQ5NAgAzMzP07t0bffv2xYEDB3Dv3j0EBwdjz549xRIHEREREZMG+maZm5vjypUraNy4McaOHQt7e3s0b94cQUFBWL16da7bKCsrY/ny5Vi7di0qVqwofcAHgDJlysDX1xeZmZno1auXwn1Xr14dGhoaGDt2LGrUqIG6detiz549+O2336TJyxUrVkRoaCgyMzPx448/wsHBAaNHj4aenh7KlMn/v56Pjw+io6NRqVKlfK96rF69Gp06dcLQoUNhY2ODAQMGSFc8ihoHERERkSAW94xSom9Yv3798PTpUxw6dKikQ/mqpKSkQFdXF5emHIKWWt6Tw4noPdvJTUo6BCIi6f07OTkZOjo6RWqLA5yJACQnJ+PatWvYsWMHEwYiIiKijzBpIALQtm1bXLp0CYMHD0bz5s1LOhwiIiKirwqTBiKgQLdXJSIiIiqtOAuSiIiIiIgUYtJAREREREQKMWkgIiIiIiKFmDQQEREREZFCTBqIiIiIiEgh3j2JiArMepx7kR8OQ0RERN8eXmkgIiIiIiKFmDQQEREREZFCTBqIiIiIiEghJg1ERERERKQQkwYiIiIiIlKISQMRERERESnEW64SUYHNmzcPMpmspMMg+mxmzJhR0iEQEX2VeKWBiIiIiIgUYtJAREREREQKMWkgIiIiIiKFmDQQEREREZFCTBqIiIiIiEghJg1ERERERKQQkwYiIiIiIlKISQMRERERESnEpIGIiIiIiBRi0kBUCgmCgAMHDpR0GERERPSNYNJApMDTp08xZMgQmJqaQiaTwcjICB4eHggNDf1s+wwICIAgCAqX+Pj4Iu0jMTERLVq0KJ6AiYiI6LunXNIBEH3NOnbsiLdv32Lz5s0wNzfHv//+i6CgIDx79uyz7bNr167w9PSUXnfo0AH29vaYNWuWtM7AwKBI+zAyMirS9kRERFS68EoDUR6SkpJw9uxZzJ8/H40bN0aVKlVQu3ZtTJo0CW3atAEAxMfHQxAEREZGym0nCAKCg4MBAMHBwRAEAUFBQXB1dYWGhgbq16+P2NjYXPerrq4OIyMjaVFVVYWGhob0+sSJE6hTpw60tbVhZGSE7t2748mTJ9L2s2bNQsWKFeUSm1atWqFx48bIysoCwOFJRERE9GmYNBDlQUtLC1paWjhw4ADS09OL3N7kyZOxePFihIeHQ1lZGX379i1UOxkZGZg9ezaioqJw4MABxMfHw9fXV24/ZmZm6N+/PwBg1apVOHfuHDZv3owyZQr2Xz49PR0pKSlyCxEREZVeHJ5ElAdlZWUEBARgwIABWLNmDVxcXODu7o5u3brB0dHxk9ubM2cO3N3dAQATJ05Eq1at8ObNG6ipqX1SOx8mG+bm5li+fDlq1aqF1NRUaGlpQUlJCdu2bUONGjUwceJELF++HL/99htMTU0LvI958+Zh5syZnxQXERERfb94pYFIgY4dO+LRo0c4dOgQPD09ERwcDBcXFwQEBHxyWx8mGsbGxgAgN6yooC5fvgwvLy+YmppCW1tbSkQSEhKkOubm5li0aBHmz5+PNm3aoHv37p+0j0mTJiE5OVlaHjx48MlxEhER0feDSQNRPtTU1NC8eXNMnToV586dg6+vL6ZPnw4A0nAfURSl+hkZGbm2o6KiIv0sCAIASHMMCiotLQ0eHh7Q0dHB9u3bERYWhv379wMA3r59K1f377//hpKSEuLj4/Hu3btP2o9MJoOOjo7cQkRERKUXkwaiT1S9enWkpaUB+L+7GCUmJkrlH06KLm43b97Es2fP4O/vj4YNG8LGxibXqxW7d+9GYGAggoODkZCQgNmzZ3+2mIiIiOj7xzkNRHl49uwZOnfujL59+8LR0RHa2toIDw/HggUL0LZtWwDv73RUt25d+Pv7o2rVqnjy5AmmTJny2WIyNTWFqqoqVqxYgcGDB+P69es5EoKHDx9iyJAhmD9/Pn744Qds2rQJrVu3RosWLVC3bt3PFhsRERF9v3ilgSgPWlpaqFOnDn755Re4ubnB3t4eU6dOxYABA7By5Uqp3saNG/Hu3TvUrFkTo0ePxs8///zZYjIwMEBAQAD27t2L6tWrw9/fH4sWLZLKRVGEr68vateujeHDhwMAPDw8MGTIEPTo0QOpqamfLTYiIiL6fgnih4OxiYhykZKSAl1dXUycOBEymaykwyH6bGbMmFHSIRARFZvs9+/k5OQiz0/klQYiIiIiIlKISQMRERERESnEpIGIiIiIiBRi0kBERERERAoxaSAiIiIiIoWYNBARERERkUJMGoiIiIiISCEmDUREREREpBAf7kZE+SrOh8MQERHRl8GHuxERERER0RfDpIGIiIiIiBRi0kBERERERAoxaSAiIiIiIoWYNBARERERkUJMGoiIiIiISCHlkg6AiL4dgfsbQ0NDqaTDICqyLp0vlXQIRETfFF5pICIiIiIihZg0EBERERGRQkwaiIiIiIhIISYNRERERESkEJMGIiIiIiJSiEkDEREREREpxKSBiIiIiIgUYtJAREREREQKMWkgIiIiIiKFmDSUMr6+vmjXrl1Jh0FERERE35BSmTQ8ffoUQ4YMgampKWQyGYyMjODh4YHQ0FCpjiAIOHDgwGeLwdfXF4IgwN/fX279gQMHIAhCkduPj4+HIAiIjIwsclsBAQEQBAG2trY5yvbu3QtBEGBmZlbk/ZQ2AQEB0NPTK3K9giSCERER6Ny5MwwNDaGmpgZLS0sMGDAAt27d+rSgiYiIqFQqlUlDx44dERERgc2bN+PWrVs4dOgQGjVqhGfPnhX7vt6+fZtnmZqaGubPn48XL158sX0WlqamJp48eYLz58/Lrd+wYQNMTU2LfX9UfA4fPoy6desiPT0d27dvR0xMDLZt2wZdXV1MnTq1pMMjIiKib0CpSxqSkpJw9uxZzJ8/H40bN0aVKlVQu3ZtTJo0CW3atAEA6Vvz9u3by32LfufOHbRt2xaGhobQ0tJCrVq1cPLkSbn2zczMMHv2bPTq1Qs6OjoYOHBgnrE0a9YMRkZGmDdvnsKY9+3bBzs7O8hkMpiZmWHx4sX57rNq1aoAAGdnZwiCgEaNGslts2jRIhgbG0NfXx/Dhg1DRkaGwhiUlZXRvXt3bNy4UVr38OFDBAcHo3v37nJ1C3qc5s6di759+0JbWxumpqZYt26dXJ0JEybAysoKGhoaMDc3x9SpU3PE+fPPP6NChQrQ1tZG//79MXHiRNSoUUOuzm+//QZbW1uoqanBxsYGv/76q1SWfUVmz549aNiwIdTV1VGrVi3cunULYWFhcHV1hZaWFlq0aIGnT59+cruBgYFo3LgxNDQ04OTkJCVdwcHB6NOnD5KTkyEIAgRBwIwZMxSeg8J49eoV+vTpg5YtW+LQoUNo1qwZqlatijp16mDRokVYu3Ztse+TiIiIvj+lLmnQ0tKClpYWDhw4gPT09FzrhIWFAQA2bdqExMRE6XVqaipatmyJoKAgREREwNPTE15eXkhISJDbftGiRXByckJERITCb3KVlJQwd+5crFixAg8fPsy1zuXLl9GlSxd069YN165dw4wZMzB16lQEBAQo3OelS5cAACdPnkRiYiICAwOluqdPn8adO3dw+vRpbN68GQEBATnay03fvn2xZ88evHr1CsD7YTOenp4wNDSUq1fQ47R48WK4uroiIiICQ4cOxZAhQxAbGyuVa2trIyAgANHR0Vi2bBnWr1+PX375RSrfvn075syZg/nz5+Py5cswNTXF6tWr5faxfft2TJs2DXPmzEFMTAzmzp2LqVOnYvPmzXL1pk+fjilTpuDKlStSgjR+/HgsW7YMZ8+eRVxcHKZNm/bJ7U6ePBl+fn6IjIyElZUVvL298e7dO9SvXx9Lly6Fjo4OEhMTkZiYCD8/v3zPwac6fvw4/vvvP4wfPz7X8ryGPaWnpyMlJUVuISIiotKr1CUNysrKCAgIwObNm6Gnp4cGDRrgf//7H65evSrVMTAwAPD+A5WRkZH02snJCYMGDYK9vT0sLS0xe/ZsVKtWDYcOHZLbR5MmTTB27FhUq1YN1apVUxhP+/btUaNGDUyfPj3X8iVLlqBp06aYOnUqrKys4Ovri+HDh2PhwoUK95kds76+PoyMjFCuXDmpbtmyZbFy5UrY2NigdevWaNWqFYKCgvI9ds7OzjA3N8fvv/8OURQREBCAvn375qhX0OPUsmVLDB06FBYWFpgwYQLKly+P06dPS+VTpkxB/fr1YWZmBi8vL/j5+WHPnj1S+YoVK9CvXz/06dMHVlZWmDZtGhwcHOT2MX36dCxevBgdOnRA1apV0aFDB/z00085vmH38/ODh4cHbG1tMWrUKFy+fBlTp05FgwYN4OzsjH79+snF9inttmrVClZWVpg5cybu37+PuLg4qKqqQldXF4IgwMjICEZGRtDS0sr3HHyq27dvAwBsbGw+abt58+ZBV1dXWkxMTIo9NiIiIvp2lLqkAXg/p+HRo0c4dOgQPD09ERwcDBcXl3y/bU9NTYWfnx9sbW2hp6cHLS0txMTE5PgG3dXV9ZPimT9/PjZv3oyYmJgcZTExMWjQoIHcugYNGuD27dvIzMws1D7t7OygpKQkvTY2NsaTJ08KtG3fvn2xadMmnDlzBmlpaWjZsmWOOgU9To6OjtLP2R+eP4xj9+7daNCggfSBesqUKXJtxMbGonbt2nJtfvg6LS0Nd+7cQb9+/aQrTFpaWvj5559x586dPGPJvnLyYQJiaGgoxVbYdo2NjQGgwMe6OIiiWKjtJk2ahOTkZGl58OBBMUdGRERE3xLlkg6gpKipqaF58+Zo3rw5pk6div79+2P69Onw9fXNcxs/Pz+cOHECixYtgoWFBdTV1dGpU6ccE481NTU/KRY3Nzd4eHhg0qRJCvevyKfsU0VFRe61IAjIysoq0LY+Pj4YP348ZsyYgZ49e0JZOeevUEGPk6I4zp8/Dx8fH8ycORMeHh7Q1dXFrl27csznUCQ1NRUAsH79etSpU0eu7MOk6eNYsu9e9fG67NiK2m5Bj3VxsLKyAgDcvHkT9erVK/B2MpkMMpnsc4VFRERE35hSmzR8rHr16nK3WFVRUZH7Jh8AQkND4evri/bt2wN4/+ExPj6+WPbv7++PGjVqwNraWm69ra2t3K1gs+OwsrLK8QH1Q6qqqgCQow9FVa5cObRp0wZ79uzBmjVrcq1THMfp3LlzqFKlCiZPniytu3//vlwda2trhIWFoVevXtK67PknwPurAxUrVsTdu3fh4+PzSftXpLjaVVVVLfbz87Eff/wR5cuXx4IFC7B///4c5UlJSQW67SsRERGVbqUuaXj27Bk6d+6Mvn37wtHREdra2ggPD8eCBQvQtm1bqZ6ZmRmCgoLQoEEDyGQylC1bFpaWlggMDISXlxcEQcDUqVOL7VtjBwcH+Pj4YPny5XLrx44di1q1amH27Nno2rUrzp8/j5UrV8rdqSc3FSpUgLq6Oo4dO4bKlStDTU0Nurq6xRJrQEAAfv31V+jr6+daXhzHydLSEgkJCdi1axdq1aqFP//8M8eH3hEjRmDAgAFwdXVF/fr1sXv3bly9ehXm5uZSnZkzZ2LkyJHQ1dWFp6cn0tPTER4ejhcvXmDMmDGf3vlibNfMzAypqakICgqCk5MTNDQ0oKGhkWvdzMzMHM/ckMlk0rMzkpOTc5Tr6+vDxMQEv/32Gzp37ow2bdpg5MiRsLCwwH///Yc9e/ZIx5iIiIhIkVI3p0FLSwt16tTBL7/8Ajc3N9jb22Pq1KkYMGAAVq5cKdVbvHgxTpw4ARMTEzg7OwN4Pym5bNmyqF+/Pry8vODh4QEXF5dii23WrFk5Ply7uLhgz5492LVrF+zt7TFt2jTMmjUr32FMysrKWL58OdauXYuKFSvKJURFpa6unmfCABTPcWrTpg1++uknDB8+HDVq1MC5c+dy3InKx8cHkyZNgp+fH1xcXHDv3j34+vpCTU1NqtO/f3/89ttv2LRpExwcHODu7o6AgADplrSFVRzt1q9fH4MHD0bXrl1hYGCABQsW5Fk3NTUVzs7OcouXl5dUHhwcnKN85syZAIC2bdvi3LlzUFFRQffu3WFjYwNvb28kJyfj559/LvxBICIiolJDEAs7U5LoK9S8eXMYGRlh69atJR3KdyUlJQW6urrYFOACDY28h8URfSu6dL5U0iEQEX122e/fycnJ0NHRKVJbpW54En0/Xr16hTVr1sDDwwNKSkrYuXMnTp48iRMnTpR0aERERETfFSYN9M0SBAFHjhzBnDlz8ObNG1hbW2Pfvn1o1qxZSYdGRERE9F1h0kDfLHV1dZw8ebKkwyAiIiL67pW6idBERERERPRpmDQQEREREZFCTBqIiIiIiEghJg1ERERERKQQkwYiIiIiIlKId08iogLr0P50kR8OQ0RERN8eXmkgIiIiIiKFmDQQEREREZFCTBqIiIiIiEghJg1ERERERKQQkwYiIiIiIlKISQMRERERESnEW64SUYHVP3ASShqaJR0GfYOiOnmUdAhERFQEvNJAREREREQKMWkgIiIiIiKFmDQQEREREZFCTBqIiIiIiEghJg1ERERERKQQkwYiIiIiIlKISQMRERERESnEpIGIiIiIiBRi0kBERERERAp99UlDfHw8BEFAZGRkSYcCABAEAQcOHABQsNiCg4MhCAKSkpK+SHxfiq+vL9q1a1fSYRTajBkzUKNGja+mHSIiIqKvWYkmDb6+vhAEQVr09fXh6emJq1evfvFYZsyYIcWhpKQEExMTDBw4EM+fP5erl5iYiBYtWnzx+D7Vh8c1t2XGjBlFan/ZsmUICAgoUF0bGxvIZDI8fvy4SPssTn5+fggKCvqkbT5MGIvSTmGYmZlh6dKln30/RERERLkp8SsNnp6eSExMRGJiIoKCgqCsrIzWrVuXSCx2dnZITExEQkICNm3ahGPHjmHIkCFydYyMjCCTyUokvk+RfUwTExOxdOlS6OjoyK3z8/MrUvu6urrQ09PLt15ISAhev36NTp06YfPmzUXaZ3HS0tKCvr7+V9MOERER0desxJMGmUwGIyMjGBkZoUaNGpg4cSIePHiAp0+f5lo/ICAgx4fVAwcOQBAEuXUHDx6Ei4sL1NTUYG5ujpkzZ+Ldu3cKY1FWVoaRkREqVaqEZs2aoXPnzjhx4oRcndy+bb558ybq168PNTU12Nvb48yZMznaDg0NhaOjI9TU1FC3bl1cv35drnzfvn2ws7ODTCaDmZkZFi9eLFduZmaGuXPnom/fvtDW1oapqSnWrVuXZ1+yj6mRkRF0dXUhCIL0Oi0tDT4+PjA0NISWlhZq1aqFkydPyvVHQ0MDO3bskNbt2bMH6urqiI6OBlDw4UkbNmxA9+7d0bNnT2zcuDFH+a+//gpLS0uoqanB0NAQnTp1ksp+//13ODg4QF1dHfr6+mjWrBnS0tIAAFlZWZg1axYqV64MmUyGGjVq4NixY3JtP3z4EN7e3ihXrhw0NTXh6uqKixcvAsg5rCgsLAzNmzdH+fLloaurC3d3d1y5ckXu+ANA+/btIQiC9PrjdvKLK3tIW2BgIBo3bgwNDQ04OTnh/Pnz+R7LDwmCgN9++w3t27eHhoYGLC0tcejQIbk6N27cQOvWraGjowNtbW00bNgQd+7c+aT9EBEREQFfQdLwodTUVGzbtg0WFhZF+vb27Nmz6NWrF0aNGoXo6GisXbsWAQEBmDNnToHbiI+Px/Hjx6Gqqppv3XHjxmHs2LGIiIhAvXr14OXlhWfPnuWos3jxYoSFhcHAwABeXl7IyMgAAFy+fBldunRBt27dcO3aNcyYMQNTp07NMfxn8eLFcHV1RUREBIYOHYohQ4YgNja2wH3KlpqaipYtWyIoKAgRERHw9PSEl5cXEhISALwfTrRo0SIMHToUCQkJePjwIQYPHoz58+ejevXqBd7Py5cvsXfvXvTo0QPNmzdHcnIyzp49K5WHh4dj5MiRmDVrFmJjY3Hs2DG4ubkBeH+lxNvbG3379kVMTAyCg4PRoUMHiKII4P3wqMWLF2PRokW4evUqPDw80KZNG9y+fVvqo7u7O/755x8cOnQIUVFRGD9+PLKysvKMtXfv3ggJCcGFCxdgaWmJli1b4uXLlwDeJxUAsGnTJiQmJkqvP5ZfXNkmT54MPz8/REZGwsrKCt7e3vkmtR+bOXMmunTpgqtXr6Jly5bw8fGRhtP9888/cHNzg0wmw6lTp3D58mX07du3wPtIT09HSkqK3EJERESll3JJB3D48GFoaWkBANLS0mBsbIzDhw+jTJnC5zMzZ87ExIkT0bt3bwCAubk5Zs+ejfHjx2P69Ol5bnft2jVoaWkhMzMTb968AQAsWbIk3/0NHz4cHTt2BACsXr0ax44dw4YNGzB+/HipzvTp09G8eXMAwObNm1G5cmXs378fXbp0wZIlS9C0aVNMnToVAGBlZYXo6GgsXLgQvr6+UhstW7bE0KFDAQATJkzAL7/8gtOnT8Pa2voTjg7g5OQEJycn6fXs2bOxf/9+HDp0CMOHDwcADB06FEeOHEGPHj2gqqqKWrVqYcSIEZ+0n127dsHS0hJ2dnYAgG7dumHDhg1o2LAhACAhIQGamppo3bo1tLW1UaVKFTg7OwN4nzS8e/cOHTp0QJUqVQAADg4OUtuLFi3ChAkT0K1bNwDA/Pnzcfr0aSxduhSrVq3Cjh078PTpU4SFhaFcuXIAAAsLizxjbdKkidzrdevWQU9PD2fOnEHr1q1hYGAAANDT04ORkVGe7eQXVzY/Pz+0atUKwPvfVzs7O8TFxcHGxia/wyrx9fWFt7c3AGDu3LlYvnw5Ll26BE9PT6xatQq6urrYtWsXVFRUALz/vSqoefPmYebMmQWuT0RERN+3Er/S0LhxY0RGRiIyMhKXLl2Ch4cHWrRogfv37xe6zaioKMyaNQtaWlrSMmDAACQmJuLVq1d5bmdtbY3IyEiEhYVhwoQJ8PDwKNAH5Xr16kk/Kysrw9XVFTExMXnWKVeuHKytraU6MTExaNCggVz9Bg0a4Pbt28jMzJTWOTo6Sj9nDzd68uRJvvF9LDU1FX5+frC1tYWenh60tLQQExMjXWnItnHjRly9ehVXrlxBQEBAjiFg+dm4cSN69Oghve7Rowf27t0rfXvfvHlzVKlSBebm5ujZsye2b98unR8nJyc0bdoUDg4O6Ny5M9avX48XL14AAFJSUvDo0aNcj1n2MY2MjISzs7OUMOTn33//xYABA2BpaQldXV3o6OggNTU1xzFRpCBxZfvwXBobGwPAJ5/LD9vQ1NSEjo6O1EZkZCQaNmwoJQyfatKkSUhOTpaWBw8eFKodIiIi+j6UeNKgqakJCwsLWFhYoFatWvjtt9+QlpaG9evX51q/TJky0hCVbNnDfLKlpqZi5syZUjISGRmJa9eu4fbt21BTU8szFlVVVVhYWMDe3h7+/v5QUlL6qr5t/fgDoCAIeQ63UcTPzw/79+/H3LlzcfbsWURGRsLBwQFv376VqxcVFYW0tDSkpaUhMTHxk/YRHR2NCxcuYPz48VBWVoaysjLq1q2LV69eYdeuXQAAbW1tXLlyBTt37oSxsTGmTZsGJycnJCUlQUlJCSdOnMDRo0dRvXp1rFixAtbW1rh3716B9q+urv5J8fbu3RuRkZFYtmwZzp07h8jISOjr6+c4JsXlw3OZnYx96rlU9Pvwqf3/mEwmg46OjtxCREREpVeJJw0fEwQBZcqUwevXr3MtNzAwwMuXL6UJsQByPCfBxcUFsbGxUjLy4fIpw56mTJmCRYsW4dGjRwrrXbhwQfr53bt3uHz5MmxtbfOs8+LFC9y6dUuqY2tri9DQULn6oaGhsLKygpKSUoHjLajQ0FD4+vqiffv2cHBwgJGREeLj4+XqPH/+HL6+vpg8eTJ8fX3h4+OT5znJzYYNG+Dm5oaoqCi55G3MmDHYsGGDVE9ZWRnNmjXDggULcPXqVcTHx+PUqVMA3v8uNGjQADNnzkRERARUVVWxf/9+6OjooGLFirkes+w5F46OjoiMjMxxy1xFx2TkyJFo2bKlNCH9v//+k6ujoqIid+XnYwWJ60txdHTE2bNncyTURERERIVR4klDeno6Hj9+jMePHyMmJgYjRoxAamoqvLy8cq1fp04daGho4H//+x/u3LmDHTt25JgwPG3aNGzZsgUzZ87EjRs3EBMTg127dmHKlCmfFFu9evXg6OiIuXPnKqy3atUq7N+/Hzdv3sSwYcPw4sUL9O3bV67OrFmzEBQUhOvXr8PX1xfly5eX7j40duxYBAUFYfbs2bh16xY2b96MlStXFvm2qHmxtLREYGAgIiMjERUVhe7du+f4lnvw4MEwMTHBlClTsGTJEmRmZhY4noyMDGzduhXe3t6wt7eXW/r374+LFy/ixo0bOHz4MJYvX47IyEjcv38fW7ZsQVZWFqytrXHx4kXMnTsX4eHhSEhIQGBgIJ4+fSolWuPGjcP8+fOxe/duxMbGYuLEiYiMjMSoUaMAAN7e3jAyMkK7du0QGhqKu3fvYt++fXnepcjS0hJbt25FTEwMLl68CB8fnxzf1puZmSEoKAiPHz+Whkp9LL+4vpThw4cjJSUF3bp1Q3h4OG7fvo2tW7dKE+f379//SfMniIiIqHQr8aTh2LFjMDY2hrGxMerUqYOwsDDs3bsXjRo1yrV+uXLlsG3bNhw5cgQODg7YuXNnjgeVeXh44PDhw/jrr79Qq1Yt1K1bF7/88os0ofZT/PTTT/jtt98Ujun29/eHv78/nJycEBISgkOHDqF8+fI56owaNQo1a9bE48eP8ccff0h3ZnJxccGePXuwa9cu2NvbY9q0aZg1a5bcJOjitGTJEpQtWxb169eHl5cXPDw84OLiIpVv2bIFR44cwdatW6GsrAxNTU1s27YN69evx9GjR/Nt/9ChQ3j27Bnat2+fo8zW1ha2trbYsGED9PT0EBgYiCZNmsDW1hZr1qzBzp07YWdnBx0dHfz9999o2bIlrKysMGXKFCxevFh6sN7IkSMxZswYjB07Fg4ODjh27BgOHToES0tLAO+Hmv3111+oUKECWrZsCQcHB2nIWW42bNiAFy9ewMXFBT179sTIkSNRoUIFuTqLFy/GiRMnYGJiIk3Y/lh+cX0p+vr6OHXqlHQXqZo1a2L9+vXSkKbk5ORC3XmLiIiISidB/HiCABHRR1JSUqCrqwu7zfugpKFZ0uHQNyiqk0dJh0BEVOpkv38nJycXeX5iiV9pICIiIiKirxuTBiIiIiIiUohJAxERERERKcSkgYiIiIiIFGLSQERERERECjFpICIiIiIihZg0EBERERGRQkwaiIiIiIhIIeWSDoCIvh3n2jUr8sNhiIiI6NvDKw1ERERERKQQkwYiIiIiIlKIw5OIKF+iKAIAUlJSSjgSIiIiKqjs9+3s9/GiYNJARPl69uwZAMDExKSEIyEiIqJP9fLlS+jq6hapDSYNRJSvcuXKAQASEhKK/EfnW5CSkgITExM8ePCg1Ez8Lm19Zn+/f6Wtz+zv968wfRZFES9fvkTFihWLvH8mDUSUrzJl3k9/0tXVLTV/nAFAR0enVPUXKH19Zn+/f6Wtz+zv9+9T+1xcX/ZxIjQRERERESnEpIGIiIiIiBRi0kBE+ZLJZJg+fTpkMllJh/JFlLb+AqWvz+zv96+09Zn9/f6VdJ8FsTjuwURERERERN8tXmkgIiIiIiKFmDQQEREREZFCTBqIiIiIiEghJg1EpNCqVatgZmYGNTU11KlTB5cuXSrpkApk3rx5qFWrFrS1tVGhQgW0a9cOsbGxcnUaNWoEQRDklsGDB8vVSUhIQKtWraChoYEKFSpg3LhxePfunVyd4OBguLi4QCaTwcLCAgEBAZ+7eznMmDEjR19sbGyk8jdv3mDYsGHQ19eHlpYWOnbsiH///VeujW+lr9nMzMxy9FkQBAwbNgzAt39+//77b3h5eaFixYoQBAEHDhyQKxdFEdOmTYOxsTHU1dXRrFkz3L59W67O8+fP4ePjAx0dHejp6aFfv35ITU2Vq3P16lU0bNgQampqMDExwYIFC3LEsnfvXtjY2EBNTQ0ODg44cuTIF+1vRkYGJkyYAAcHB2hqaqJixYro1asXHj16JNdGbr8T/v7+X2V/8+szAPj6+uboj6enp1yd7+UcA8j1/7MgCFi4cKFU51s6xwV5H/qSf5uL/H4uEhHlYdeuXaKqqqq4ceNG8caNG+KAAQNEPT098d9//y3p0PLl4eEhbtq0Sbx+/boYGRkptmzZUjQ1NRVTU1OlOu7u7uKAAQPExMREaUlOTpbK3717J9rb24vNmjUTIyIixCNHjojly5cXJ02aJNW5e/euqKGhIY4ZM0aMjo4WV6xYISopKYnHjh37ov2dPn26aGdnJ9eXp0+fSuWDBw8WTUxMxKCgIDE8PFysW7euWL9+/W+yr9mePHki198TJ06IAMTTp0+Lovjtn98jR46IkydPFgMDA0UA4v79++XK/f39RV1dXfHAgQNiVFSU2KZNG7Fq1ari69evpTqenp6ik5OTeOHCBfHs2bOihYWF6O3tLZUnJyeLhoaGoo+Pj3j9+nVx586dorq6urh27VqpTmhoqKikpCQuWLBAjI6OFqdMmSKqqKiI165d+2L9TUpKEps1aybu3r1bvHnzpnj+/Hmxdu3aYs2aNeXaqFKlijhr1iy5c/7h//mvqb/59VkURbF3796ip6enXH+eP38uV+d7OceiKMr1MzExUdy4caMoCIJ4584dqc63dI4L8j70pf42F8f7OZMGIspT7dq1xWHDhkmvMzMzxYoVK4rz5s0rwagK58mTJyIA8cyZM9I6d3d3cdSoUXluc+TIEbFMmTLi48ePpXWrV68WdXR0xPT0dFEURXH8+PGinZ2d3HZdu3YVPTw8ircD+Zg+fbro5OSUa1lSUpKooqIi7t27V1oXExMjAhDPnz8viuK31de8jBo1SqxWrZqYlZUliuL3dX4//oCVlZUlGhkZiQsXLpTWJSUliTKZTNy5c6coiqIYHR0tAhDDwsKkOkePHhUFQRD/+ecfURRF8ddffxXLli0r9VcURXHChAmitbW19LpLly5iq1at5OKpU6eOOGjQoGLt44dy+0D5sUuXLokAxPv370vrqlSpIv7yyy95bvO19lcUc+9z7969xbZt2+a5zfd+jtu2bSs2adJEbt23fI4/fh/6kn+bi+P9nMOTiChXb9++xeXLl9GsWTNpXZkyZdCsWTOcP3++BCMrnOTkZABAuXLl5NZv374d5cuXh729PSZNmoRXr15JZefPn4eDgwMMDQ2ldR4eHkhJScGNGzekOh8eo+w6JXGMbt++jYoVK8Lc3Bw+Pj5ISEgAAFy+fBkZGRlycdrY2MDU1FSK81vr68fevn2Lbdu2oW/fvhAEQVr/PZ3fD927dw+PHz+Wi01XVxd16tSRO6d6enpwdXWV6jRr1gxlypTBxYsXpTpubm5QVVWV6nh4eCA2NhYvXryQ6nyNxyA5ORmCIEBPT09uvb+/P/T19eHs7IyFCxfKDeP4FvsbHByMChUqwNraGkOGDMGzZ8+ksu/5HP/777/4888/0a9fvxxl3+o5/vh96Ev9bS6u93PlT+ksEZUe//33HzIzM+X+UAGAoaEhbt68WUJRFU5WVhZGjx6NBg0awN7eXlrfvXt3VKlSBRUrVsTVq1cxYcIExMbGIjAwEADw+PHjXPufXaaoTkpKCl6/fg11dfXP2TVJnTp1EBAQAGtrayQmJmLmzJlo2LAhrl+/jsePH0NVVTXHhytDQ8N8+5FdpqjOl+5rbg4cOICkpCT4+vpK676n8/ux7Phyi+3D2CtUqCBXrqysjHLlysnVqVq1ao42ssvKli2b5zHIbqMkvHnzBhMmTIC3tzd0dHSk9SNHjoSLiwvKlSuHc+fOYdKkSUhMTMSSJUsAfHv99fT0RIcOHVC1alXcuXMH//vf/9CiRQucP38eSkpK3/U53rx5M7S1tdGhQwe59d/qOc7tfehL/W1+8eJFsbyfM2kgou/esGHDcP36dYSEhMitHzhwoPSzg4MDjI2N0bRpU9y5cwfVqlX70mEWSYsWLaSfHR0dUadOHVSpUgV79uwp0Q/zX8qGDRvQokULVKxYUVr3PZ1f+j8ZGRno0qULRFHE6tWr5crGjBkj/ezo6AhVVVUMGjQI8+bN+yafHNytWzfpZwcHBzg6OqJatWoIDg5G06ZNSzCyz2/jxo3w8fGBmpqa3Ppv9Rzn9T70LeHwJCLKVfny5aGkpJTjLg7//vsvjIyMSiiqTzd8+HAcPnwYp0+fRuXKlRXWrVOnDgAgLi4OAGBkZJRr/7PLFNXR0dEp0Q/renp6sLKyQlxcHIyMjPD27VskJSXJ1fnwXH7Lfb1//z5OnjyJ/v37K6z3PZ3f7PgU/f80MjLCkydP5MrfvXuH58+fF8t5L4m/A9kJw/3793HixAm5qwy5qVOnDt69e4f4+HgA315/P2Zubo7y5cvL/Q5/b+cYAM6ePYvY2Nh8/08D38Y5zut96Ev9bS6u93MmDUSUK1VVVdSsWRNBQUHSuqysLAQFBaFevXolGFnBiKKI4cOHY//+/Th16lSOy9W5iYyMBAAYGxsDAOrVq4dr167JvSlnf1CpXr26VOfDY5Rdp6SPUWpqKu7cuQNjY2PUrFkTKioqcnHGxsYiISFBivNb7uumTZtQoUIFtGrVSmG97+n8Vq1aFUZGRnKxpaSk4OLFi3LnNCkpCZcvX5bqnDp1CllZWVICVa9ePfz999/IyMiQ6pw4cQLW1tYoW7asVOdrOAbZCcPt27dx8uRJ6Ovr57tNZGQkypQpIw3h+Zb6m5uHDx/i2bNncr/D39M5zrZhwwbUrFkTTk5O+db9ms9xfu9DX+pvc7G9nxd4yjQRlTq7du0SZTKZGBAQIEZHR4sDBw4U9fT05O7i8LUaMmSIqKurKwYHB8vdmu/Vq1eiKIpiXFycOGvWLDE8PFy8d++eePDgQdHc3Fx0c3OT2si+1d2PP/4oRkZGiseOHRMNDAxyvdXduHHjxJiYGHHVqlUlchvSsWPHisHBweK9e/fE0NBQsVmzZmL58uXFJ0+eiKL4/rZ+pqam4qlTp8Tw8HCxXr16Yr169b7Jvn4oMzNTNDU1FSdMmCC3/ns4vy9fvhQjIiLEiIgIEYC4ZMkSMSIiQrpbkL+/v6inpycePHhQvHr1qti2bdtcb7nq7OwsXrx4UQwJCREtLS3lbseZlJQkGhoaij179hSvX78u7tq1S9TQ0Mhxe0plZWVx0aJFYkxMjDh9+vTPcntKRf19+/at2KZNG7Fy5cpiZGSk3P/p7DvInDt3Tvzll1/EyMhI8c6dO+K2bdtEAwMDsVevXl9lf/Pr88uXL0U/Pz/x/Pnz4r1798STJ0+KLi4uoqWlpfjmzRupje/lHGdLTk4WNTQ0xNWrV+fY/ls7x/m9D4nil/vbXBzv50waiEihFStWiKampqKqqqpYu3Zt8cKFCyUdUoEAyHXZtGmTKIqimJCQILq5uYnlypUTZTKZaGFhIY4bN07uPv6iKIrx8fFiixYtRHV1dbF8+fLi2LFjxYyMDLk6p0+fFmvUqCGqqqqK5ubm0j6+pK5du4rGxsaiqqqqWKlSJbFr165iXFycVP769Wtx6NChYtmyZUUNDQ2xffv2YmJiolwb30pfP3T8+HERgBgbGyu3/ns4v6dPn871d7h3796iKL6/7erUqVNFQ0NDUSaTiU2bNs1xHJ49eyZ6e3uLWlpaoo6OjtinTx/x5cuXcnWioqLEH374QZTJZGKlSpVEf3//HLHs2bNHtLKyElVVVUU7Ozvxzz///KL9vXfvXp7/p7Ofy3H58mWxTp06oq6urqimpiba2tqKc+fOlfuA/TX1N78+v3r1Svzxxx9FAwMDUUVFRaxSpYo4YMCAHB/yvpdznG3t2rWiurq6mJSUlGP7b+0c5/c+JIpf9m9zUd/Phf/fKSIiIiIiolxxTgMRERERESnEpIGIiIiIiBRi0kBERERERAoxaSAiIiIiIoWYNBARERERkUJMGoiIiIiISCEmDUREREREpBCTBiIiIiIiUohJAxERUQmYMWMGatSoUdJhEBEVCJ8ITUREVIwaNWqEGjVqYOnSpQrrpaamIj09Hfr6+l8msG/YjBkzcODAAURGRpZ0KESllnJJB0BERFSaiKKIzMxMaGlpQUtLq6TDISIqEA5PIiIiKia+vr44c+YMli1bBkEQIAgCAgICIAgCjh49ipo1a0ImkyEkJCTH8CRfX1+0a9cOM2fOhIGBAXR0dDB48GC8fftWqvP777/DwcEB6urq0NfXR7NmzZCWlpZvXGFhYWjevDnKly8PXV1duLu748qVK3J1BEHA2rVr0bp1a2hoaMDW1hbnz59HXFwcGjVqBE1NTdSvXx937tyR22716tWoVq0aVFVVYW1tja1bt0pl8fHxEARB7gpBUlISBEFAcHAwACA4OBiCICAoKAiurq7Q0NBA/fr1ERsbCwAICAjAzJkzERUVJXdMiejLYtJARERUTJYtW4Z69ephwIABSExMRGJiIkxMTAAAEydOhL+/P2JiYuDo6Jjr9kFBQYiJiUFwcDB27tyJwMBAzJw5EwCQmJgIb29v9O3bV6rToUMHFGSU8cuXL9G7d2+EhITgwoULsLS0RMuWLfHy5Uu5erNnz0avXr0QGRkJGxsbdO/eHYMGDcKkSZMQHh4OURQxfPhwqf7+/fsxatQojB07FtevX8egQYPQp08fnD59+pOP3eTJk7F48WKEh4dDWVkZffv2BQB07doVY8eOhZ2dnXRMu3bt+sntE1ERiURERFRs3N3dxVGjRkmvT58+LQIQDxw4IFdv+vTpopOTk/S6d+/eYrly5cS0tDRp3erVq0UtLS0xMzNTvHz5sghAjI+PL3KMmZmZora2tvjHH39I6wCIU6ZMkV6fP39eBCBu2LBBWrdz505RTU1Nel2/fn1xwIABcm137txZbNmypSiKonjv3j0RgBgRESGVv3jxQgQgnj59WhTF/zs+J0+elOr8+eefIgDx9evXoijmPFZE9OXxSgMREdEX4Orqmm8dJycnaGhoSK/r1auH1NRUPHjwAE5OTmjatCkcHBzQuXNnrF+/Hi9evCjQvv/9918MGDAAlpaW0NXVhY6ODlJTU5GQkCBX78MrIIaGhgAABwcHuXVv3rxBSkoKACAmJgYNGjSQa6NBgwaIiYkpUFx57dvY2BgA8OTJk09uh4g+DyYNREREX4CmpmaRtldSUsKJEydw9OhRVK9eHStWrIC1tTXu3buX77a9e/dGZGQkli1bhnPnziEyMhL6+vpy8yUAQEVFRfpZEIQ812VlZRUo5jJl3n/MED8YQpWRkZFr3aLsh4g+PyYNRERExUhVVRWZmZmF2jYqKgqvX7+WXl+4cAFaWlrSvAhBENCgQQPMnDkTERERUFVVxf79+/NtNzQ0FCNHjkTLli1hZ2cHmUyG//77r1AxfsjW1hahoaE59lW9enUAgIGBAYD38zGyFea2qUU5pkRUPHjLVSIiomJkZmaGixcvIj4+HlpaWp/0bfnbt2/Rr18/TJkyBfHx8Zg+fTqGDx+OMmXK4OLFiwgKCsKPP/6IChUq4OLFi3j69ClsbW3zbdfS0hJbt26Fq6srUlJSMG7cOKirqxelm/h/7d0vr8FRHMfxzyaYDe2nEAgEEopkjIBgNhvJ5hGYQPsFQRAIooComU2VeAIegE1XeADCbbfcOfe6bO6f92s76Zyd8z3xs3POjiR1u13VajXF43Hl83mt12stl0ttNhtJksvlUiqV0mAwUCgU0ul0km3bd68TDAZ1PB613+8VCATk8XjkdDofrh/A13HSAADAE3U6HTkcDkWjUVmW9eHdgEkul1M4HFY6nVa9Xle5XFav15Mkeb1ebbdblUolRSIR2bat0WikYrH46bzT6VTn81mJREKNRkOtVks+n++7W3xXqVQ0Ho81HA4Vi8U0mUw0n8+VyWTex8xmM12vVyWTSbXbbfX7/bvXqVarKhQKymazsixLi8Xi4doB3IcfoQEA+AGazaYul4tWq9WrSwGADzhpAAAAAGBEaAAA4Jdzu9032263e3V5AP4AricBAPDLHQ6Hm31+v/8pj54B/G+EBgAAAABGXE8CAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGBEaAAAAABgRGgAAAAAYERoAAAAAGD0BlzNOusiPHtvAAAAAElFTkSuQmCC\n"},"metadata":{}}],"source":["sns.barplot(data=top_10_companies, y='company_name', x='trips_amount', orient='h');"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"w_KhHPiHtyFN","outputId":"a7189eb8-198a-47dd-82f4-7b795b437a04","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930786191,"user_tz":-420,"elapsed":29,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Flash Cab                            1\n","Taxi Affiliation Services            1\n","Medallion Leasin                     1\n","Yellow Cab                           1\n","Taxi Affiliation Service Yellow      1\n","Chicago Carriage Cab Corp            1\n","City Service                         1\n","Sun Taxi                             1\n","Star North Management LLC            1\n","Blue Ribbon Taxi Association Inc.    1\n","Name: company_name, dtype: int64"]},"metadata":{},"execution_count":29}],"source":["top_10_companies['company_name'].value_counts()"]},{"cell_type":"markdown","metadata":{"id":"-4Rfbpt3tyFN"},"source":["**Conclusion**\n","\n","- The taxi company that has the largest number of trips is occupied by Flash Cab with a `trips_amount` at 19558\n","- While the number 10 position is occupied by Blue Ribbon Taxi Association Inc., with a `trips_amount` at 5953"]},{"cell_type":"markdown","metadata":{"id":"h7FT-PE0tyFN"},"source":["# Stage 5. Testing the Hypothesis\n","\n","`/content/project_sql_result_07.csv` — the result of the last query. This file contains travel data from the Loop to O'Hare International Airport. Remember, the following are the values for the columns in this table:\n","\n","- `start_ts` — pick-up date and time\n","- `weather_conditions` — the weather conditions when the trip started\n","- `duration_seconds` — duration of the trip in seconds\n","\n","Test the following hypotheses:\n","\n","\"The average trip duration from the Loop to O'Hare International Airport changes on rainy Saturdays.\"\n","\n","Determine independently how big the level of significance (alpha).\n","\n","Explain:\n","\n","- How do you formulate the null hypothesis and the alternative hypothesis\n","- What criteria did you use to test your hypothesis and why did you use them"]},{"cell_type":"markdown","metadata":{"id":"6YkYj44vtyFN"},"source":["# 5.1 import the dataset and check the outliers"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"miI9UF1BtyFN"},"outputs":[],"source":["loop_ohare = pd.read_csv('/content/project_sql_result_07.csv')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VFwDj6cjtyFO","outputId":"e01b5d27-15a9-4861-9c82-438cdae143c9","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1689930786192,"user_tz":-420,"elapsed":27,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["              start_ts weather_conditions  duration_seconds\n","0  2017-11-25 16:00:00               Good            2410.0\n","1  2017-11-25 14:00:00               Good            1920.0\n","2  2017-11-25 12:00:00               Good            1543.0\n","3  2017-11-04 10:00:00               Good            2512.0\n","4  2017-11-11 07:00:00               Good            1440.0"],"text/html":["\n","\n","  <div id=\"df-22583bb5-e66e-4dfa-aed3-7f2ad0f561c0\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>start_ts</th>\n","      <th>weather_conditions</th>\n","      <th>duration_seconds</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2017-11-25 16:00:00</td>\n","      <td>Good</td>\n","      <td>2410.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2017-11-25 14:00:00</td>\n","      <td>Good</td>\n","      <td>1920.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2017-11-25 12:00:00</td>\n","      <td>Good</td>\n","      <td>1543.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2017-11-04 10:00:00</td>\n","      <td>Good</td>\n","      <td>2512.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2017-11-11 07:00:00</td>\n","      <td>Good</td>\n","      <td>1440.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22583bb5-e66e-4dfa-aed3-7f2ad0f561c0')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-c12a9ac4-c17e-4938-b594-704f1ab46853\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c12a9ac4-c17e-4938-b594-704f1ab46853')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-c12a9ac4-c17e-4938-b594-704f1ab46853 button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-22583bb5-e66e-4dfa-aed3-7f2ad0f561c0 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-22583bb5-e66e-4dfa-aed3-7f2ad0f561c0');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":31}],"source":["loop_ohare.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7WZQ-vEqtyFO","outputId":"6a92e024-03ab-47c6-d122-4bd7dad7cee3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930786192,"user_tz":-420,"elapsed":25,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["11    1068\n","Name: start_ts, dtype: int64"]},"metadata":{},"execution_count":32}],"source":["pd.to_datetime(loop_ohare['start_ts']).dt.month.value_counts()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ec-rS0cmtyFO","outputId":"0aaf2e49-e11d-4e23-9048-deddda039b0a","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930786193,"user_tz":-420,"elapsed":24,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["11    426\n","4     367\n","18    180\n","25     95\n","Name: start_ts, dtype: int64"]},"metadata":{},"execution_count":33}],"source":["pd.to_datetime(loop_ohare['start_ts']).dt.day.value_counts()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6YHa-N-ityFO","outputId":"4711b287-91a5-4127-814d-882b459325c5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930786194,"user_tz":-420,"elapsed":24,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Good    888\n","Bad     180\n","Name: weather_conditions, dtype: int64"]},"metadata":{},"execution_count":34}],"source":["loop_ohare['weather_conditions'].value_counts()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FbnD1raBtyFO","outputId":"b7e0c289-dded-4b35-b470-6e9ceb77fafe","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930786194,"user_tz":-420,"elapsed":22,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1068, 3)"]},"metadata":{},"execution_count":35}],"source":["loop_ohare.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zjDTfMf0tyFP","outputId":"5e011ea5-9366-4c87-c6ac-93dc7c0e6d96","colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"status":"ok","timestamp":1689930786194,"user_tz":-420,"elapsed":21,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["       duration_seconds\n","count       1068.000000\n","mean        2071.731273\n","std          769.461125\n","min            0.000000\n","25%         1438.250000\n","50%         1980.000000\n","75%         2580.000000\n","max         7440.000000"],"text/html":["\n","\n","  <div id=\"df-256f54e7-c2e3-4e22-83c9-36f5f80a4794\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>duration_seconds</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>1068.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>2071.731273</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>769.461125</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>1438.250000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>1980.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>2580.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>7440.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-256f54e7-c2e3-4e22-83c9-36f5f80a4794')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-c1ed5a78-5fa8-4276-8524-f415aa931c22\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c1ed5a78-5fa8-4276-8524-f415aa931c22')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-c1ed5a78-5fa8-4276-8524-f415aa931c22 button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-256f54e7-c2e3-4e22-83c9-36f5f80a4794 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-256f54e7-c2e3-4e22-83c9-36f5f80a4794');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":36}],"source":["loop_ohare.describe()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ktzZAH__tyFP","outputId":"c22dc9e5-1566-49d6-cf69-d1bb938b8dea","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930786194,"user_tz":-420,"elapsed":20,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1068 entries, 0 to 1067\n","Data columns (total 3 columns):\n"," #   Column              Non-Null Count  Dtype  \n","---  ------              --------------  -----  \n"," 0   start_ts            1068 non-null   object \n"," 1   weather_conditions  1068 non-null   object \n"," 2   duration_seconds    1068 non-null   float64\n","dtypes: float64(1), object(2)\n","memory usage: 25.2+ KB\n"]}],"source":["loop_ohare.info()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"y4RJByFItyFP","outputId":"66288901-decd-4252-d057-435329785156","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930786195,"user_tz":-420,"elapsed":19,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["start_ts              0\n","weather_conditions    0\n","duration_seconds      0\n","dtype: int64"]},"metadata":{},"execution_count":38}],"source":["loop_ohare.isnull().sum()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DklxpvKAtyFP","outputId":"05ac4cbe-2ae1-4201-d160-f9b8e5046b76","colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"status":"ok","timestamp":1689930786195,"user_tz":-420,"elapsed":18,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                 start_ts weather_conditions  duration_seconds\n","0     2017-11-25 16:00:00               Good            2410.0\n","1     2017-11-25 14:00:00               Good            1920.0\n","2     2017-11-25 12:00:00               Good            1543.0\n","3     2017-11-04 10:00:00               Good            2512.0\n","4     2017-11-11 07:00:00               Good            1440.0\n","...                   ...                ...               ...\n","1063  2017-11-25 11:00:00               Good               0.0\n","1064  2017-11-11 10:00:00               Good            1318.0\n","1065  2017-11-11 13:00:00               Good            2100.0\n","1066  2017-11-11 08:00:00               Good            1380.0\n","1067  2017-11-04 16:00:00                Bad            2834.0\n","\n","[1068 rows x 3 columns]"],"text/html":["\n","\n","  <div id=\"df-6013c217-246d-4bde-8408-510fe1f1313d\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>start_ts</th>\n","      <th>weather_conditions</th>\n","      <th>duration_seconds</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2017-11-25 16:00:00</td>\n","      <td>Good</td>\n","      <td>2410.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2017-11-25 14:00:00</td>\n","      <td>Good</td>\n","      <td>1920.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2017-11-25 12:00:00</td>\n","      <td>Good</td>\n","      <td>1543.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2017-11-04 10:00:00</td>\n","      <td>Good</td>\n","      <td>2512.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2017-11-11 07:00:00</td>\n","      <td>Good</td>\n","      <td>1440.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1063</th>\n","      <td>2017-11-25 11:00:00</td>\n","      <td>Good</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1064</th>\n","      <td>2017-11-11 10:00:00</td>\n","      <td>Good</td>\n","      <td>1318.0</td>\n","    </tr>\n","    <tr>\n","      <th>1065</th>\n","      <td>2017-11-11 13:00:00</td>\n","      <td>Good</td>\n","      <td>2100.0</td>\n","    </tr>\n","    <tr>\n","      <th>1066</th>\n","      <td>2017-11-11 08:00:00</td>\n","      <td>Good</td>\n","      <td>1380.0</td>\n","    </tr>\n","    <tr>\n","      <th>1067</th>\n","      <td>2017-11-04 16:00:00</td>\n","      <td>Bad</td>\n","      <td>2834.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1068 rows × 3 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6013c217-246d-4bde-8408-510fe1f1313d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-43984d50-b688-4a6a-9e6d-7fc47f6affb9\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-43984d50-b688-4a6a-9e6d-7fc47f6affb9')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-43984d50-b688-4a6a-9e6d-7fc47f6affb9 button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-6013c217-246d-4bde-8408-510fe1f1313d button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-6013c217-246d-4bde-8408-510fe1f1313d');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":39}],"source":["loop_ohare"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-LMEr4YgtyFP","outputId":"3fb9a6eb-a97a-41c8-ed02-327aaa7b03cf","colab":{"base_uri":"https://localhost:8080/","height":450},"executionInfo":{"status":"ok","timestamp":1689930786590,"user_tz":-420,"elapsed":412,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["sns.boxplot(x='duration_seconds', y='weather_conditions', data=loop_ohare);"]},{"cell_type":"markdown","metadata":{"id":"DpMvAu07tyFP"},"source":["**Conclusion**\n","\n","There are outliers with extreme values, so we need to take it out"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"e6Mh4Wq6tyFP","outputId":"2e80f6dd-39e5-432c-d637-b3618dc27fb4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930786591,"user_tz":-420,"elapsed":16,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Q1:  1438.25\n","Q3:  2580.0\n","IQR:  1141.75\n","min:  0\n","max:  4292.625\n"]}],"source":["#Remove outliers\n","Q1 = loop_ohare['duration_seconds'].quantile(0.25)\n","Q3 = loop_ohare['duration_seconds'].quantile(0.75)\n","IQR = Q3 - Q1\n","lower_whisker = Q1 - (1.5 * IQR)\n","upper_whisker = Q3 + (1.5 * IQR)\n","\n","if lower_whisker < 0:\n","    lower_whisker = 0\n","\n","print('Q1: ', Q1)\n","print('Q3: ', Q3)\n","print('IQR: ', IQR)\n","print('min: ', lower_whisker)\n","print('max: ', upper_whisker)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"R49vlTwVtyFQ"},"outputs":[],"source":["loop_ohare_without_outliers = loop_ohare[(loop_ohare['duration_seconds']>lower_whisker) &\n","                                         (loop_ohare['duration_seconds']<upper_whisker)]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KIaBFmRXtyFQ","outputId":"70fe2813-93a9-41ea-dab2-810f2e372d1a","colab":{"base_uri":"https://localhost:8080/","height":450},"executionInfo":{"status":"ok","timestamp":1689930786592,"user_tz":-420,"elapsed":14,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["sns.boxplot(x='duration_seconds', y='weather_conditions', data=loop_ohare_without_outliers);"]},{"cell_type":"markdown","metadata":{"id":"bU5yD9IXtyFR"},"source":["**Conclusion**\n","\n","There is no outliers with extreme values, so we can proceed to the hypothesis testing stage"]},{"cell_type":"markdown","metadata":{"id":"dXs07lHJtyFR"},"source":["# 5.2 Test the hypothesis"]},{"cell_type":"markdown","metadata":{"id":"-xvKZWK6tyFR"},"source":["Test the following hypotheses:\n","\n","\"The average trip duration from the Loop to O'Hare International Airport has been changed on rainy days at Saturdays.\"\n","\n","- Null Hypothesis (H0): \"The average trip duration from the Loop to O'Hare International Airport is SAME on Saturday.\"\n","- Alternate Hypothesis (H1): \"Average trip duration from the Loop to O'Hare International Airport DID NOT SAME/CHANGED on Saturday.\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GHEVOWyOtyFR","outputId":"5de434a7-d1bc-40f6-9e66-239e8fca3924","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1689930786592,"user_tz":-420,"elapsed":13,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["              start_ts weather_conditions  duration_seconds\n","0  2017-11-25 16:00:00               Good            2410.0\n","1  2017-11-25 14:00:00               Good            1920.0\n","2  2017-11-25 12:00:00               Good            1543.0\n","3  2017-11-04 10:00:00               Good            2512.0\n","4  2017-11-11 07:00:00               Good            1440.0"],"text/html":["\n","\n","  <div id=\"df-e7445ce9-c444-422d-bea7-e8e26e0a9275\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>start_ts</th>\n","      <th>weather_conditions</th>\n","      <th>duration_seconds</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2017-11-25 16:00:00</td>\n","      <td>Good</td>\n","      <td>2410.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2017-11-25 14:00:00</td>\n","      <td>Good</td>\n","      <td>1920.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2017-11-25 12:00:00</td>\n","      <td>Good</td>\n","      <td>1543.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2017-11-04 10:00:00</td>\n","      <td>Good</td>\n","      <td>2512.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2017-11-11 07:00:00</td>\n","      <td>Good</td>\n","      <td>1440.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7445ce9-c444-422d-bea7-e8e26e0a9275')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-f58f9b18-c423-47bc-a078-1651889c44ef\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f58f9b18-c423-47bc-a078-1651889c44ef')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-f58f9b18-c423-47bc-a078-1651889c44ef button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-e7445ce9-c444-422d-bea7-e8e26e0a9275 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-e7445ce9-c444-422d-bea7-e8e26e0a9275');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":44}],"source":["loop_ohare_without_outliers.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8phhqOZ_tyFR","outputId":"889cf8da-2b14-4df4-fbd2-f7291b83b6d5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930787402,"user_tz":-420,"elapsed":822,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["2017-11-11    421\n","2017-11-04    365\n","2017-11-18    178\n","2017-11-25     92\n","Name: start_ts, dtype: int64"]},"metadata":{},"execution_count":45}],"source":["pd.to_datetime(loop_ohare_without_outliers['start_ts']).dt.date.value_counts()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b4GR9EFHtyFR","outputId":"1fa08e12-dbae-467c-9a37-7d221b51cae9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930787402,"user_tz":-420,"elapsed":21,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Good    877\n","Bad     179\n","Name: weather_conditions, dtype: int64"]},"metadata":{},"execution_count":46}],"source":["loop_ohare_without_outliers['weather_conditions'].value_counts()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SuhjOW_ctyFR"},"outputs":[],"source":["good_weather_data = loop_ohare_without_outliers[loop_ohare_without_outliers['weather_conditions']=='Good'].reset_index(drop=True)\n","bad_weather_data = loop_ohare_without_outliers[loop_ohare_without_outliers['weather_conditions']=='Bad'].reset_index(drop=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vYYajvxwtyFR","outputId":"0768b933-766d-4bb5-dfe2-05c394c9d126","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930787403,"user_tz":-420,"elapsed":20,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["((877, 3), (179, 3))"]},"metadata":{},"execution_count":48}],"source":["good_weather_data.shape, bad_weather_data.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YPZJz-petyFS","outputId":"e8491778-a8ff-49cb-e00b-acb94c9cddb2","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1689930787404,"user_tz":-420,"elapsed":18,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["              start_ts weather_conditions  duration_seconds\n","0  2017-11-25 16:00:00               Good            2410.0\n","1  2017-11-25 14:00:00               Good            1920.0\n","2  2017-11-25 12:00:00               Good            1543.0\n","3  2017-11-04 10:00:00               Good            2512.0\n","4  2017-11-11 07:00:00               Good            1440.0"],"text/html":["\n","\n","  <div id=\"df-e75ac82b-0ff1-4e47-ba53-8bbad9c13ced\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>start_ts</th>\n","      <th>weather_conditions</th>\n","      <th>duration_seconds</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2017-11-25 16:00:00</td>\n","      <td>Good</td>\n","      <td>2410.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2017-11-25 14:00:00</td>\n","      <td>Good</td>\n","      <td>1920.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2017-11-25 12:00:00</td>\n","      <td>Good</td>\n","      <td>1543.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2017-11-04 10:00:00</td>\n","      <td>Good</td>\n","      <td>2512.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2017-11-11 07:00:00</td>\n","      <td>Good</td>\n","      <td>1440.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e75ac82b-0ff1-4e47-ba53-8bbad9c13ced')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-6da478ba-2575-42c1-b797-0aaf1b7dff04\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6da478ba-2575-42c1-b797-0aaf1b7dff04')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-6da478ba-2575-42c1-b797-0aaf1b7dff04 button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 'block' : 'none';\n","}\n","\n","        displayQuickchartButton(document);\n","      </script>\n","      <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-e75ac82b-0ff1-4e47-ba53-8bbad9c13ced button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-e75ac82b-0ff1-4e47-ba53-8bbad9c13ced');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":49}],"source":["good_weather_data.head()"]},{"cell_type":"markdown","metadata":{"id":"HW0COR5ytyFS"},"source":["Show `good_weather_data` and `bad_weather_data` variances"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Qnemef5OtyFS","outputId":"f26582ef-c631-4b43-d4d5-9503cf469fad","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930787404,"user_tz":-420,"elapsed":17,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["473991.7496388771"]},"metadata":{},"execution_count":50}],"source":["np.var(good_weather_data['duration_seconds'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IN73f-t2tyFS","outputId":"aa516e64-9aca-4fab-d6b3-cd5c238f9891","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930787404,"user_tz":-420,"elapsed":15,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["473991.7496388771"]},"metadata":{},"execution_count":51}],"source":["np.var(good_weather_data['duration_seconds'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jX4YO6cBtyFS"},"outputs":[],"source":["#H0 : the variances of group 1 (good weather) and group 2 (bad weather) are the same\n","#H1 : the variances of group 1 (good weather) and group 2 (bad weather) are not the same"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Kd1rA2tntyFT","outputId":"82b10e5b-9f01-444b-e1a0-bd4ec9979859","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930787404,"user_tz":-420,"elapsed":13,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["p-value:  0.7888178851804917\n","Fail to Reject H0: We don`t have enough evidence to say that variance of sample 1 and sample 2 are not the same\n"]}],"source":["alpha = 0.05\n","\n","p_value_levene = st.levene(good_weather_data['duration_seconds'], bad_weather_data['duration_seconds']).pvalue\n","print('p-value: ', p_value_levene)\n","\n","if p_value_levene < alpha:\n","    print('Reject H0: variance of sample 1 is not equal to variance of sample 2')\n","else:\n","    print('Fail to Reject H0: We don`t have enough evidence to say that variance of sample 1 and sample 2 are not the same')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mCLbtJzgtyFT","outputId":"4125fe90-eab2-4713-80cf-4c67abf61f84","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1689930787405,"user_tz":-420,"elapsed":12,"user":{"displayName":"Guntur Wibawa","userId":"10578009354531389415"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["p-value 2.3968233040077725e-13\n","We reject the null hypothesis\n"]}],"source":["p_value_ttest = st.ttest_ind(good_weather_data['duration_seconds'],\n","                             bad_weather_data['duration_seconds'], equal_var=True).pvalue\n","print('p-value', p_value_ttest)\n","\n","if(p_value_ttest < alpha):\n","    print(\"We reject the null hypothesis\")\n","else:\n","    print(\"We can`t reject the null hypothesis\")"]},{"cell_type":"markdown","metadata":{"id":"lofO6xXXtyFT"},"source":["**Conclusion**\n","\n","In Levene's p-value test we find that there is a strong correlation between `good_weather_data` and `bad_weather_data`. And then, in the independent ttest we get that from the Loop to the International Airport the duration has been changed (Reject H0)"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.5"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":true,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":false,"toc_position":{},"toc_section_display":true,"toc_window_display":false},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":0}