hotel-booking-dataset-practices.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/bobaktech/hotel-booking-dataset-practices?scriptVersionId=104588172\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"markdown","id":"behavioral-bedroom","metadata":{"papermill":{"duration":0.023763,"end_time":"2022-08-31T09:38:27.484033","exception":false,"start_time":"2022-08-31T09:38:27.46027","status":"completed"},"tags":[]},"source":["# Introduction\n","> The hotel-booking dataset contains 119390 observations for a City Hotel and a Resort Hotel. Each observation represents a hotel booking between the 1st of July 2015 and 31st of August 2017, including booking that effectively arrived and booking that were canceled. <br>\n","The data is originally from the article Hotel Booking Demand Datasets, written by Nuno Antonio, Ana Almeida, and Luis Nunes for Data in Brief, Volume 22, February 2019.\n","\n","In this notebook, I have done some exercises with this dataset given from [Filoger](filoger.com)'s AI Bootcamp which I had signed up before. <br>\n","Some plots are displayed using Seaborn library, also.\n","\n","# Dataset's Content\n","> Since this is hotel real data, all data elements pertaining hotel or costumer identification were deleted.\n","Four Columns, 'name', 'email', 'phone number' and 'credit_card' have been artificially created and added to the dataset."]},{"cell_type":"markdown","id":"greater-graphic","metadata":{"papermill":{"duration":0.021351,"end_time":"2022-08-31T09:38:27.527514","exception":false,"start_time":"2022-08-31T09:38:27.506163","status":"completed"},"tags":[]},"source":["# Importing necessary libraries"]},{"cell_type":"code","execution_count":1,"id":"blond-hepatitis","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:27.575205Z","iopub.status.busy":"2022-08-31T09:38:27.573969Z","iopub.status.idle":"2022-08-31T09:38:28.430588Z","shell.execute_reply":"2022-08-31T09:38:28.429637Z","shell.execute_reply.started":"2021-08-17T21:48:35.262262Z"},"papermill":{"duration":0.881569,"end_time":"2022-08-31T09:38:28.430714","exception":false,"start_time":"2022-08-31T09:38:27.549145","status":"completed"},"tags":[]},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","%matplotlib inline"]},{"cell_type":"markdown","id":"certified-leather","metadata":{"papermill":{"duration":0.021555,"end_time":"2022-08-31T09:38:28.474449","exception":false,"start_time":"2022-08-31T09:38:28.452894","status":"completed"},"tags":[]},"source":["# Q1 - Reading the dataset's CSV file"]},{"cell_type":"code","execution_count":2,"id":"broken-explorer","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:28.522836Z","iopub.status.busy":"2022-08-31T09:38:28.520928Z","iopub.status.idle":"2022-08-31T09:38:29.215689Z","shell.execute_reply":"2022-08-31T09:38:29.215183Z","shell.execute_reply.started":"2021-08-17T21:48:36.165968Z"},"papermill":{"duration":0.719144,"end_time":"2022-08-31T09:38:29.215807","exception":false,"start_time":"2022-08-31T09:38:28.496663","status":"completed"},"tags":[]},"outputs":[],"source":["df = pd.read_csv(\"../input/hotel-booking/hotel_booking.csv\");"]},{"cell_type":"markdown","id":"stuffed-album","metadata":{"papermill":{"duration":0.02126,"end_time":"2022-08-31T09:38:29.259043","exception":false,"start_time":"2022-08-31T09:38:29.237783","status":"completed"},"tags":[]},"source":["By using Pandas library and read_csv() function, the dataset's csv file is called and added as a dataframe into a variable named 'df'."]},{"cell_type":"markdown","id":"finnish-ebony","metadata":{"papermill":{"duration":0.022889,"end_time":"2022-08-31T09:38:29.303918","exception":false,"start_time":"2022-08-31T09:38:29.281029","status":"completed"},"tags":[]},"source":["# Q2 - Dataset's Base information"]},{"cell_type":"code","execution_count":3,"id":"contemporary-money","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:29.350433Z","iopub.status.busy":"2022-08-31T09:38:29.349941Z","iopub.status.idle":"2022-08-31T09:38:29.437251Z","shell.execute_reply":"2022-08-31T09:38:29.436669Z","shell.execute_reply.started":"2021-08-17T21:48:37.201874Z"},"papermill":{"duration":0.111671,"end_time":"2022-08-31T09:38:29.437377","exception":false,"start_time":"2022-08-31T09:38:29.325706","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 119390 entries, 0 to 119389\n","Data columns (total 36 columns):\n"," #   Column                          Non-Null Count   Dtype  \n","---  ------                          --------------   -----  \n"," 0   hotel                           119390 non-null  object \n"," 1   is_canceled                     119390 non-null  int64  \n"," 2   lead_time                       119390 non-null  int64  \n"," 3   arrival_date_year               119390 non-null  int64  \n"," 4   arrival_date_month              119390 non-null  object \n"," 5   arrival_date_week_number        119390 non-null  int64  \n"," 6   arrival_date_day_of_month       119390 non-null  int64  \n"," 7   stays_in_weekend_nights         119390 non-null  int64  \n"," 8   stays_in_week_nights            119390 non-null  int64  \n"," 9   adults                          119390 non-null  int64  \n"," 10  children                        119386 non-null  float64\n"," 11  babies                          119390 non-null  int64  \n"," 12  meal                            119390 non-null  object \n"," 13  country                         118902 non-null  object \n"," 14  market_segment                  119390 non-null  object \n"," 15  distribution_channel            119390 non-null  object \n"," 16  is_repeated_guest               119390 non-null  int64  \n"," 17  previous_cancellations          119390 non-null  int64  \n"," 18  previous_bookings_not_canceled  119390 non-null  int64  \n"," 19  reserved_room_type              119390 non-null  object \n"," 20  assigned_room_type              119390 non-null  object \n"," 21  booking_changes                 119390 non-null  int64  \n"," 22  deposit_type                    119390 non-null  object \n"," 23  agent                           103050 non-null  float64\n"," 24  company                         6797 non-null    float64\n"," 25  days_in_waiting_list            119390 non-null  int64  \n"," 26  customer_type                   119390 non-null  object \n"," 27  adr                             119390 non-null  float64\n"," 28  required_car_parking_spaces     119390 non-null  int64  \n"," 29  total_of_special_requests       119390 non-null  int64  \n"," 30  reservation_status              119390 non-null  object \n"," 31  reservation_status_date         119390 non-null  object \n"," 32  name                            119390 non-null  object \n"," 33  email                           119390 non-null  object \n"," 34  phone-number                    119390 non-null  object \n"," 35  credit_card                     119390 non-null  object \n","dtypes: float64(4), int64(16), object(16)\n","memory usage: 32.8+ MB\n"]}],"source":["df.info()"]},{"cell_type":"markdown","id":"traditional-thesis","metadata":{"papermill":{"duration":0.02286,"end_time":"2022-08-31T09:38:29.48371","exception":false,"start_time":"2022-08-31T09:38:29.46085","status":"completed"},"tags":[]},"source":["The info() function displays the base informations of the dataframe which contains type of the dataframe, total number of records and range of indexes, columns name, total number of not null (non-null) values and types of columns and the memory usage of the dataframe.\n"]},{"cell_type":"markdown","id":"polish-hampshire","metadata":{"papermill":{"duration":0.022028,"end_time":"2022-08-31T09:38:29.528795","exception":false,"start_time":"2022-08-31T09:38:29.506767","status":"completed"},"tags":[]},"source":["# Q3 - Dataset's number of rows"]},{"cell_type":"code","execution_count":4,"id":"administrative-sailing","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:29.590536Z","iopub.status.busy":"2022-08-31T09:38:29.589949Z","iopub.status.idle":"2022-08-31T09:38:29.606089Z","shell.execute_reply":"2022-08-31T09:38:29.606633Z","shell.execute_reply.started":"2021-08-17T21:48:37.427038Z"},"papermill":{"duration":0.051709,"end_time":"2022-08-31T09:38:29.606806","exception":false,"start_time":"2022-08-31T09:38:29.555097","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>number_of_rows</th>\n","      <th>number_of_columns</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>119390</td>\n","      <td>36</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   number_of_rows  number_of_columns\n","0          119390                 36"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["pd.DataFrame([df.shape],columns=['number_of_rows','number_of_columns'],index=['0'])"]},{"cell_type":"markdown","id":"employed-circus","metadata":{"papermill":{"duration":0.022597,"end_time":"2022-08-31T09:38:29.652366","exception":false,"start_time":"2022-08-31T09:38:29.629769","status":"completed"},"tags":[]},"source":["The shape attribute represents the total number of columns and rows as a tuple. <br>\n","I decided to show them as tabular data, so I putted them in a dataframe function."]},{"cell_type":"markdown","id":"modular-kitty","metadata":{"papermill":{"duration":0.021847,"end_time":"2022-08-31T09:38:29.696736","exception":false,"start_time":"2022-08-31T09:38:29.674889","status":"completed"},"tags":[]},"source":["# Q4 - Does the dataset have any missing data? If the answer is positive, which column have the most missing data?\n","> Solution source: [yun.ir/dxig38](yun.ir/dxig38)"]},{"cell_type":"code","execution_count":5,"id":"passive-canon","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:29.743931Z","iopub.status.busy":"2022-08-31T09:38:29.74345Z","iopub.status.idle":"2022-08-31T09:38:29.872187Z","shell.execute_reply":"2022-08-31T09:38:29.871795Z","shell.execute_reply.started":"2021-08-17T21:48:37.446548Z"},"papermill":{"duration":0.152888,"end_time":"2022-08-31T09:38:29.872295","exception":false,"start_time":"2022-08-31T09:38:29.719407","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>check_missing_data_existance</th>\n","      <th>column_with_maximum_missing_data</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>True</td>\n","      <td>COMPANY</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   check_missing_data_existance column_with_maximum_missing_data\n","0                          True                          COMPANY"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# Q4-1 - Checking missing data\n","checkMD = df.isnull().values.any();\n","\n","# Q4-2 - The column with most missing data\n","columnMD = df.isnull().sum().idxmax();\n","\n","# Get the output\n","pd.DataFrame({\n","    'check_missing_data_existance': checkMD,\n","    'column_with_maximum_missing_data': columnMD.upper()\n","},index=['0'])"]},{"cell_type":"markdown","id":"bigger-alarm","metadata":{"papermill":{"duration":0.023071,"end_time":"2022-08-31T09:38:29.918106","exception":false,"start_time":"2022-08-31T09:38:29.895035","status":"completed"},"tags":[]},"source":["For checking the existance of missing data in any record, I used the 'isnull()' function. The result will show \"NaN\" (Not a number). To present the result as boolean, the 'values' attribute is added. <br>\n","Now, we had found the records with missing data. There are several records founded whereas only one is enough to check for the existance. In this case, I used the 'any()' function to say if there is atleast one missing data, return true, else return false. At last, the result is stored in 'checkMD' variable.\n","\n","To find the column with the most missing data, I used the 'isnull()' again but this time, I summed the missing data exist in a column using 'sum()' function. To show the column name which have the most missing data, I used the 'idxmax()' function.\n","\n","To visualize the total missing data of each column, expand the cell below. (Source: [yun.ir/h4j3m6](yun.ir/h4j3m6))"]},{"cell_type":"code","execution_count":6,"id":"medium-attraction","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.execute_input":"2022-08-31T09:38:29.96812Z","iopub.status.busy":"2022-08-31T09:38:29.96764Z","iopub.status.idle":"2022-08-31T09:38:30.544327Z","shell.execute_reply":"2022-08-31T09:38:30.543749Z","shell.execute_reply.started":"2021-08-17T21:48:37.840499Z"},"papermill":{"duration":0.604053,"end_time":"2022-08-31T09:38:30.544434","exception":false,"start_time":"2022-08-31T09:38:29.940381","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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size 3600x400 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# Calculating number of missing data by percentage\n","missing_values = 100*(df.isnull().sum() / len(df));\n","\n","# Creating a DataFrame for the columns\n","missing_values = missing_values.to_frame();\n","missing_values.columns = ['Total missing data'];\n","missing_values.index.names = ['Columns'];\n","missing_values = missing_values.reset_index();\n","\n","# Displaying the results in a bar-plot using Seaborn library\n","plt.figure(figsize=(36,4), dpi=100);\n","sns.set_style(style='ticks');\n","sns.barplot(data=missing_values, x='Columns', y='Total missing data');\n","plt.xticks(rotation = 45);\n","plt.ylabel('Total missing data (%)');"]},{"cell_type":"markdown","id":"sorted-blame","metadata":{"papermill":{"duration":0.02558,"end_time":"2022-08-31T09:38:30.596274","exception":false,"start_time":"2022-08-31T09:38:30.570694","status":"completed"},"tags":[]},"source":["# Q5 - Drop 'company' column from DataFrame."]},{"cell_type":"code","execution_count":7,"id":"endless-decade","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:30.649948Z","iopub.status.busy":"2022-08-31T09:38:30.649463Z","iopub.status.idle":"2022-08-31T09:38:30.681117Z","shell.execute_reply":"2022-08-31T09:38:30.680646Z","shell.execute_reply.started":"2021-08-17T21:48:38.659011Z"},"papermill":{"duration":0.059057,"end_time":"2022-08-31T09:38:30.681222","exception":false,"start_time":"2022-08-31T09:38:30.622165","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["['hotel',\n"," 'is_canceled',\n"," 'lead_time',\n"," 'arrival_date_year',\n"," 'arrival_date_month',\n"," 'arrival_date_week_number',\n"," 'arrival_date_day_of_month',\n"," 'stays_in_weekend_nights',\n"," 'stays_in_week_nights',\n"," 'adults',\n"," 'children',\n"," 'babies',\n"," 'meal',\n"," 'country',\n"," 'market_segment',\n"," 'distribution_channel',\n"," 'is_repeated_guest',\n"," 'previous_cancellations',\n"," 'previous_bookings_not_canceled',\n"," 'reserved_room_type',\n"," 'assigned_room_type',\n"," 'booking_changes',\n"," 'deposit_type',\n"," 'agent',\n"," 'days_in_waiting_list',\n"," 'customer_type',\n"," 'adr',\n"," 'required_car_parking_spaces',\n"," 'total_of_special_requests',\n"," 'reservation_status',\n"," 'reservation_status_date',\n"," 'name',\n"," 'email',\n"," 'phone-number',\n"," 'credit_card']"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["# Drop the desired column\n","df.drop('company',axis=1, inplace=True);\n","\n","# Display the list of dataset's column\n","df.columns.tolist()"]},{"cell_type":"markdown","id":"directed-million","metadata":{"papermill":{"duration":0.026054,"end_time":"2022-08-31T09:38:30.733487","exception":false,"start_time":"2022-08-31T09:38:30.707433","status":"completed"},"tags":[]},"source":["Using the 'drop()' function, the specified column (at here is 'company' column) will be removed. To remove it permanently, I used the 'inplace=true' attribute in the 'drop()' function. <br>\n","I added 'columns' attribute to display the columns as an array while the company column is removed. Also, I added 'tolist()' function to display the columns as a list."]},{"cell_type":"markdown","id":"third-gambling","metadata":{"papermill":{"duration":0.025584,"end_time":"2022-08-31T09:38:30.784739","exception":false,"start_time":"2022-08-31T09:38:30.759155","status":"completed"},"tags":[]},"source":["# Q6 - Which country have the most passengers? Define the top 5 countries."]},{"cell_type":"code","execution_count":8,"id":"olympic-asian","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:30.838281Z","iopub.status.busy":"2022-08-31T09:38:30.837797Z","iopub.status.idle":"2022-08-31T09:38:30.879155Z","shell.execute_reply":"2022-08-31T09:38:30.879585Z","shell.execute_reply.started":"2021-08-17T21:48:38.700837Z"},"papermill":{"duration":0.068971,"end_time":"2022-08-31T09:38:30.87972","exception":false,"start_time":"2022-08-31T09:38:30.810749","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>adults</th>\n","      <th>children</th>\n","      <th>babies</th>\n","      <th>is_canceled</th>\n","      <th>total_passengers</th>\n","    </tr>\n","    <tr>\n","      <th>country</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>PRT</th>\n","      <td>86131</td>\n","      <td>3468.0</td>\n","      <td>437</td>\n","      <td>27519</td>\n","      <td>62512.0</td>\n","    </tr>\n","    <tr>\n","      <th>GBR</th>\n","      <td>23223</td>\n","      <td>1253.0</td>\n","      <td>92</td>\n","      <td>2453</td>\n","      <td>22115.0</td>\n","    </tr>\n","    <tr>\n","      <th>FRA</th>\n","      <td>20291</td>\n","      <td>1211.0</td>\n","      <td>77</td>\n","      <td>1934</td>\n","      <td>19645.0</td>\n","    </tr>\n","    <tr>\n","      <th>ESP</th>\n","      <td>16615</td>\n","      <td>1412.0</td>\n","      <td>126</td>\n","      <td>2177</td>\n","      <td>15976.0</td>\n","    </tr>\n","    <tr>\n","      <th>DEU</th>\n","      <td>13703</td>\n","      <td>477.0</td>\n","      <td>18</td>\n","      <td>1218</td>\n","      <td>12980.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         adults  children  babies  is_canceled  total_passengers\n","country                                                         \n","PRT       86131    3468.0     437        27519           62512.0\n","GBR       23223    1253.0      92         2453           22115.0\n","FRA       20291    1211.0      77         1934           19645.0\n","ESP       16615    1412.0     126         2177           15976.0\n","DEU       13703     477.0      18         1218           12980.0"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df['total_passengers'] = (df['adults'] + df['children'] + df['babies']) - df['is_canceled'];\n","\n","df_passengers = df[['country','adults','children','babies','is_canceled','total_passengers']].groupby('country').sum().nlargest(5,'total_passengers');\n","df_passengers"]},{"cell_type":"markdown","id":"played-blues","metadata":{"papermill":{"duration":0.030079,"end_time":"2022-08-31T09:38:30.9411","exception":false,"start_time":"2022-08-31T09:38:30.911021","status":"completed"},"tags":[]},"source":["The question wants the top 5 contries with the most total passengers. <br>\n","At first, I summed the 'adults', 'children' and 'babies' columns together. Since some passengers in the dataset had canceled their tickets, the 'is_canceled' column should be subtracted from the summation. The calculation's result is stored in a new column named 'total_passengers'.\n","\n","Then, beside displaying the results, they need to be grouped by the 'country' column and sum the columns with the 'sum()' function.\n","\n","If I use the 'max()' function to display the top 5 contries, since I displayed the dataframe with more than 1 column, it doesn't know which column should be sorted by. In this case, I used the 'nlargest()' function to tell it I want the *top 5* records sorted by the *'total_passengers'* column.\n","\n","Here we go. The top 5 countries with the most total passengers are displayed in a dataframe.\n","\n","To view the results in a plot, expand the cell below."]},{"cell_type":"code","execution_count":9,"id":"explicit-bangladesh","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.execute_input":"2022-08-31T09:38:31.017726Z","iopub.status.busy":"2022-08-31T09:38:31.014914Z","iopub.status.idle":"2022-08-31T09:38:31.131178Z","shell.execute_reply":"2022-08-31T09:38:31.13155Z","shell.execute_reply.started":"2021-08-17T21:48:38.760217Z"},"papermill":{"duration":0.164274,"end_time":"2022-08-31T09:38:31.131686","exception":false,"start_time":"2022-08-31T09:38:30.967412","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Displaying in a bar plot\n","sns.set_style(style='whitegrid');\n","sns.barplot(data=df_passengers.reset_index(), x='country', y='total_passengers');\n","plt.xticks(rotation=45);"]},{"cell_type":"markdown","id":"danish-repeat","metadata":{"papermill":{"duration":0.026501,"end_time":"2022-08-31T09:38:31.185161","exception":false,"start_time":"2022-08-31T09:38:31.15866","status":"completed"},"tags":[]},"source":["# Q7 - Define the name of the passenger who have the maximum average daily rate (ADR). How much is its price?"]},{"cell_type":"code","execution_count":10,"id":"skilled-majority","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:31.244938Z","iopub.status.busy":"2022-08-31T09:38:31.244456Z","iopub.status.idle":"2022-08-31T09:38:31.253116Z","shell.execute_reply":"2022-08-31T09:38:31.25352Z","shell.execute_reply.started":"2021-08-17T21:48:38.939744Z"},"papermill":{"duration":0.040771,"end_time":"2022-08-31T09:38:31.253663","exception":false,"start_time":"2022-08-31T09:38:31.212892","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Name</th>\n","      <th>Price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Max(ADR)</th>\n","      <td>Daniel Walter</td>\n","      <td>5400.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                   Name   Price\n","Max(ADR)  Daniel Walter  5400.0"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["name = df['name'][df['adr'].idxmax()];\n","price = df['adr'].max();\n","\n","pd.DataFrame({\n","    'Name': name,\n","    'Price': price\n","},index=['Max(ADR)'])"]},{"cell_type":"markdown","id":"current-rescue","metadata":{"papermill":{"duration":0.026591,"end_time":"2022-08-31T09:38:31.307121","exception":false,"start_time":"2022-08-31T09:38:31.28053","status":"completed"},"tags":[]},"source":["The question wants to know who have the maximum ADR. For this, I first searched for the location of the maximum ADR using 'idxmax()' function. Then, I searched for the name of that passenger in the 'name' column and stored it in the 'name' variable. <br>\n","The question also wants to know the price. For this case, I found the maximum ADR and displayed it as the price.\n","\n","At last, I represented the results in a dataframe."]},{"cell_type":"markdown","id":"herbal-breast","metadata":{"papermill":{"duration":0.027104,"end_time":"2022-08-31T09:38:31.361381","exception":false,"start_time":"2022-08-31T09:38:31.334277","status":"completed"},"tags":[]},"source":["# Q8 - Average of total ADRs with 2 decimals."]},{"cell_type":"code","execution_count":11,"id":"maritime-deployment","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:31.419416Z","iopub.status.busy":"2022-08-31T09:38:31.418508Z","iopub.status.idle":"2022-08-31T09:38:31.424511Z","shell.execute_reply":"2022-08-31T09:38:31.424888Z","shell.execute_reply.started":"2021-08-17T21:48:38.957555Z"},"papermill":{"duration":0.036394,"end_time":"2022-08-31T09:38:31.42505","exception":false,"start_time":"2022-08-31T09:38:31.388656","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["101.83"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df['adr'].mean().round(2)"]},{"cell_type":"markdown","id":"delayed-capacity","metadata":{"papermill":{"duration":0.026801,"end_time":"2022-08-31T09:38:31.479666","exception":false,"start_time":"2022-08-31T09:38:31.452865","status":"completed"},"tags":[]},"source":["# Q9 - Define the average of number of nights stayed."]},{"cell_type":"code","execution_count":12,"id":"seasonal-gazette","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:31.53685Z","iopub.status.busy":"2022-08-31T09:38:31.536395Z","iopub.status.idle":"2022-08-31T09:38:31.543973Z","shell.execute_reply":"2022-08-31T09:38:31.544364Z","shell.execute_reply.started":"2021-08-17T21:48:38.968052Z"},"papermill":{"duration":0.037515,"end_time":"2022-08-31T09:38:31.5445","exception":false,"start_time":"2022-08-31T09:38:31.506985","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["3.43"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["df['total_stays_in_nights'] = df['stays_in_week_nights'] + df['stays_in_weekend_nights'];\n","\n","df['total_stays_in_nights'].mean().round(2)"]},{"cell_type":"markdown","id":"characteristic-consultation","metadata":{"papermill":{"duration":0.027494,"end_time":"2022-08-31T09:38:31.600077","exception":false,"start_time":"2022-08-31T09:38:31.572583","status":"completed"},"tags":[]},"source":["At first, to get the total number of stays in nights, I summed *stays_in_week_nights* and *stays_in_weekend_nights* columns together. <br>\n","To get the average of it, the *mean()* function is used. Also, to round the number to 2 decimals, I used the *round()* function.\n","\n","To view the total stays in nights classified by hotels, expand the cell below. Because the plot was too big, I limited it to the numbers with most stays using *xlim()* function."]},{"cell_type":"code","execution_count":13,"id":"introductory-climate","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.execute_input":"2022-08-31T09:38:31.65885Z","iopub.status.busy":"2022-08-31T09:38:31.658282Z","iopub.status.idle":"2022-08-31T09:38:32.255853Z","shell.execute_reply":"2022-08-31T09:38:32.25628Z","shell.execute_reply.started":"2021-08-17T21:48:38.983095Z"},"papermill":{"duration":0.628165,"end_time":"2022-08-31T09:38:32.256447","exception":false,"start_time":"2022-08-31T09:38:31.628282","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 1152x576 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(16,8));\n","sns.countplot(data=df, x='total_stays_in_nights', hue='hotel');\n","plt.xticks(rotation=45);\n","\n","plt.xlim(0,16);\n","plt.legend(loc=(0.3,0.9));"]},{"cell_type":"markdown","id":"massive-gnome","metadata":{"papermill":{"duration":0.027769,"end_time":"2022-08-31T09:38:32.313813","exception":false,"start_time":"2022-08-31T09:38:32.286044","status":"completed"},"tags":[]},"source":["# Q10 - Define the name and e-mail of people who had 5 special requests."]},{"cell_type":"code","execution_count":14,"id":"narrow-paint","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:32.373577Z","iopub.status.busy":"2022-08-31T09:38:32.373024Z","iopub.status.idle":"2022-08-31T09:38:32.391907Z","shell.execute_reply":"2022-08-31T09:38:32.392379Z","shell.execute_reply.started":"2021-08-17T21:48:39.808697Z"},"papermill":{"duration":0.05088,"end_time":"2022-08-31T09:38:32.39252","exception":false,"start_time":"2022-08-31T09:38:32.34164","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>name</th>\n","      <th>email</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>7860</th>\n","      <td>Amanda Harper</td>\n","      <td>Amanda.H66@yahoo.com</td>\n","    </tr>\n","    <tr>\n","      <th>11125</th>\n","      <td>Laura Sanders</td>\n","      <td>Sanders_Laura@hotmail.com</td>\n","    </tr>\n","    <tr>\n","      <th>14596</th>\n","      <td>Tommy Ortiz</td>\n","      <td>Tommy_O@hotmail.com</td>\n","    </tr>\n","    <tr>\n","      <th>14921</th>\n","      <td>Gilbert Miller</td>\n","      <td>Miller.Gilbert@aol.com</td>\n","    </tr>\n","    <tr>\n","      <th>14922</th>\n","      <td>Timothy Torres</td>\n","      <td>TTorres@protonmail.com</td>\n","    </tr>\n","    <tr>\n","      <th>24630</th>\n","      <td>Jennifer Weaver</td>\n","      <td>Jennifer_W@aol.com</td>\n","    </tr>\n","    <tr>\n","      <th>27288</th>\n","      <td>Crystal Horton</td>\n","      <td>Crystal.H@mail.com</td>\n","    </tr>\n","    <tr>\n","      <th>27477</th>\n","      <td>Brittney Burke</td>\n","      <td>Burke_Brittney16@att.com</td>\n","    </tr>\n","    <tr>\n","      <th>29906</th>\n","      <td>Cynthia Cabrera</td>\n","      <td>Cabrera.Cynthia@xfinity.com</td>\n","    </tr>\n","    <tr>\n","      <th>29949</th>\n","      <td>Sarah Floyd</td>\n","      <td>Sarah_F@gmail.com</td>\n","    </tr>\n","    <tr>\n","      <th>32267</th>\n","      <td>Michelle Villa</td>\n","      <td>Michelle.Villa@aol.com</td>\n","    </tr>\n","    <tr>\n","      <th>39027</th>\n","      <td>Nichole Hebert</td>\n","      <td>Hebert.Nichole@gmail.com</td>\n","    </tr>\n","    <tr>\n","      <th>39129</th>\n","      <td>Lindsey Mckenzie</td>\n","      <td>Lindsey.Mckenzie@att.com</td>\n","    </tr>\n","    <tr>\n","      <th>39525</th>\n","      <td>Ashley Edwards</td>\n","      <td>Edwards.Ashley@yahoo.com</td>\n","    </tr>\n","    <tr>\n","      <th>70114</th>\n","      <td>Christopher Torres</td>\n","      <td>Torres.Christopher@gmail.com</td>\n","    </tr>\n","    <tr>\n","      <th>78819</th>\n","      <td>Mrs. Tara Sullivan DVM</td>\n","      <td>Mrs..DVM@xfinity.com</td>\n","    </tr>\n","    <tr>\n","      <th>78820</th>\n","      <td>Michaela Brown</td>\n","      <td>MichaelaBrown@att.com</td>\n","    </tr>\n","    <tr>\n","      <th>78822</th>\n","      <td>Kurt Maldonado MD</td>\n","      <td>KMD15@xfinity.com</td>\n","    </tr>\n","    <tr>\n","      <th>97072</th>\n","      <td>Jason Richardson</td>\n","      <td>Jason.R@zoho.com</td>\n","    </tr>\n","    <tr>\n","      <th>97099</th>\n","      <td>Terri Hurley</td>\n","      <td>THurley@xfinity.com</td>\n","    </tr>\n","    <tr>\n","      <th>97261</th>\n","      <td>Mrs. Caitlin Webb</td>\n","      <td>Mrs._W@comcast.net</td>\n","    </tr>\n","    <tr>\n","      <th>98410</th>\n","      <td>Holly Arroyo</td>\n","      <td>Arroyo_Holly@mail.com</td>\n","    </tr>\n","    <tr>\n","      <th>98674</th>\n","      <td>Denise Campbell</td>\n","      <td>Denise_C@gmail.com</td>\n","    </tr>\n","    <tr>\n","      <th>99887</th>\n","      <td>Michael Smith</td>\n","      <td>Michael.S42@aol.com</td>\n","    </tr>\n","    <tr>\n","      <th>99888</th>\n","      <td>Dr. Trevor Sellers</td>\n","      <td>Dr._S@aol.com</td>\n","    </tr>\n","    <tr>\n","      <th>101569</th>\n","      <td>Kayla Murphy</td>\n","      <td>Kayla.Murphy@yahoo.com</td>\n","    </tr>\n","    <tr>\n","      <th>102061</th>\n","      <td>Taylor Martinez</td>\n","      <td>Taylor.Martinez@hotmail.com</td>\n","    </tr>\n","    <tr>\n","      <th>109511</th>\n","      <td>Charles Wilson</td>\n","      <td>Charles_Wilson@yahoo.com</td>\n","    </tr>\n","    <tr>\n","      <th>109590</th>\n","      <td>Tyler Allison</td>\n","      <td>Tyler.A@protonmail.com</td>\n","    </tr>\n","    <tr>\n","      <th>110082</th>\n","      <td>Matthew Bailey</td>\n","      <td>Matthew_Bailey@aol.com</td>\n","    </tr>\n","    <tr>\n","      <th>110083</th>\n","      <td>Charlotte Acevedo</td>\n","      <td>Charlotte_A@verizon.com</td>\n","    </tr>\n","    <tr>\n","      <th>111909</th>\n","      <td>Darrell Brennan</td>\n","      <td>Brennan_Darrell51@hotmail.com</td>\n","    </tr>\n","    <tr>\n","      <th>111911</th>\n","      <td>Melinda Jensen</td>\n","      <td>MelindaJensen@zoho.com</td>\n","    </tr>\n","    <tr>\n","      <th>113915</th>\n","      <td>Terry Arnold</td>\n","      <td>Arnold.Terry@zoho.com</td>\n","    </tr>\n","    <tr>\n","      <th>114770</th>\n","      <td>Mary Nguyen</td>\n","      <td>Nguyen.Mary@protonmail.com</td>\n","    </tr>\n","    <tr>\n","      <th>114909</th>\n","      <td>Lindsay Cuevas</td>\n","      <td>Lindsay.Cuevas40@mail.com</td>\n","    </tr>\n","    <tr>\n","      <th>116455</th>\n","      <td>Cynthia Hernandez</td>\n","      <td>CynthiaHernandez@xfinity.com</td>\n","    </tr>\n","    <tr>\n","      <th>116457</th>\n","      <td>Angela Hawkins</td>\n","      <td>Angela_H@gmail.com</td>\n","    </tr>\n","    <tr>\n","      <th>118817</th>\n","      <td>Sue Lawson</td>\n","      <td>Sue.L52@comcast.net</td>\n","    </tr>\n","    <tr>\n","      <th>119161</th>\n","      <td>Alyssa Richards</td>\n","      <td>Alyssa_Richards@aol.com</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                          name                          email\n","7860             Amanda Harper           Amanda.H66@yahoo.com\n","11125            Laura Sanders      Sanders_Laura@hotmail.com\n","14596              Tommy Ortiz            Tommy_O@hotmail.com\n","14921           Gilbert Miller         Miller.Gilbert@aol.com\n","14922           Timothy Torres         TTorres@protonmail.com\n","24630          Jennifer Weaver             Jennifer_W@aol.com\n","27288           Crystal Horton             Crystal.H@mail.com\n","27477           Brittney Burke       Burke_Brittney16@att.com\n","29906          Cynthia Cabrera    Cabrera.Cynthia@xfinity.com\n","29949              Sarah Floyd              Sarah_F@gmail.com\n","32267           Michelle Villa         Michelle.Villa@aol.com\n","39027           Nichole Hebert       Hebert.Nichole@gmail.com\n","39129         Lindsey Mckenzie       Lindsey.Mckenzie@att.com\n","39525           Ashley Edwards       Edwards.Ashley@yahoo.com\n","70114       Christopher Torres   Torres.Christopher@gmail.com\n","78819   Mrs. Tara Sullivan DVM           Mrs..DVM@xfinity.com\n","78820           Michaela Brown          MichaelaBrown@att.com\n","78822        Kurt Maldonado MD              KMD15@xfinity.com\n","97072         Jason Richardson               Jason.R@zoho.com\n","97099             Terri Hurley            THurley@xfinity.com\n","97261        Mrs. Caitlin Webb             Mrs._W@comcast.net\n","98410             Holly Arroyo          Arroyo_Holly@mail.com\n","98674          Denise Campbell             Denise_C@gmail.com\n","99887            Michael Smith            Michael.S42@aol.com\n","99888       Dr. Trevor Sellers                  Dr._S@aol.com\n","101569            Kayla Murphy         Kayla.Murphy@yahoo.com\n","102061         Taylor Martinez    Taylor.Martinez@hotmail.com\n","109511          Charles Wilson       Charles_Wilson@yahoo.com\n","109590           Tyler Allison         Tyler.A@protonmail.com\n","110082          Matthew Bailey         Matthew_Bailey@aol.com\n","110083       Charlotte Acevedo        Charlotte_A@verizon.com\n","111909         Darrell Brennan  Brennan_Darrell51@hotmail.com\n","111911          Melinda Jensen         MelindaJensen@zoho.com\n","113915            Terry Arnold          Arnold.Terry@zoho.com\n","114770             Mary Nguyen     Nguyen.Mary@protonmail.com\n","114909          Lindsay Cuevas      Lindsay.Cuevas40@mail.com\n","116455       Cynthia Hernandez   CynthiaHernandez@xfinity.com\n","116457          Angela Hawkins             Angela_H@gmail.com\n","118817              Sue Lawson            Sue.L52@comcast.net\n","119161         Alyssa Richards        Alyssa_Richards@aol.com"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["df[df['total_of_special_requests'] == 5][['name','email']]"]},{"cell_type":"markdown","id":"green-remove","metadata":{"papermill":{"duration":0.028644,"end_time":"2022-08-31T09:38:32.449778","exception":false,"start_time":"2022-08-31T09:38:32.421134","status":"completed"},"tags":[]},"source":["# Q11 - Which first names have the most frequency of last name? Define 5 most frequent family."]},{"cell_type":"code","execution_count":15,"id":"exciting-escape","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:32.511532Z","iopub.status.busy":"2022-08-31T09:38:32.511028Z","iopub.status.idle":"2022-08-31T09:38:32.575005Z","shell.execute_reply":"2022-08-31T09:38:32.574608Z","shell.execute_reply.started":"2021-08-17T21:48:39.844103Z"},"papermill":{"duration":0.096384,"end_time":"2022-08-31T09:38:32.575121","exception":false,"start_time":"2022-08-31T09:38:32.478737","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>value_counts</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Smith</th>\n","      <td>2510</td>\n","    </tr>\n","    <tr>\n","      <th>Johnson</th>\n","      <td>1998</td>\n","    </tr>\n","    <tr>\n","      <th>Williams</th>\n","      <td>1628</td>\n","    </tr>\n","    <tr>\n","      <th>Jones</th>\n","      <td>1441</td>\n","    </tr>\n","    <tr>\n","      <th>Brown</th>\n","      <td>1433</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          value_counts\n","Smith             2510\n","Johnson           1998\n","Williams          1628\n","Jones             1441\n","Brown             1433"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["lastname_count = df['name'].apply(lambda lname:lname.split()[1]).value_counts().head().to_frame('value_counts');\n","lastname_count"]},{"cell_type":"markdown","id":"bored-sound","metadata":{"papermill":{"duration":0.028288,"end_time":"2022-08-31T09:38:32.632549","exception":false,"start_time":"2022-08-31T09:38:32.604261","status":"completed"},"tags":[]},"source":["In order to get the last names, at the first step, we should <u>split</u> the names via *split()* function. Hopefully, the prefixes (Such as Mr, Mrs, Dr, etc) are at the end of the names. But I don't know if anyone's name is in two parts or not (E.g. 'Mohammad Ali', a Persian name). If any exists, I don't know what to do. So, lets assume that the first and last names are in single part. :-)\n","\n","To split each name in the column, I created a function and applied it via *apply()* function.\n","\n","Since the indexes start from 0, then:\n","* 0 => First names\n","* 1 => Last names\n","* 2 => Prefixes\n","\n","So, the index 1 will be choosed to have the last names.\n","\n","At last, we should count the last names via *value_counts()* function, i.e. how much of each last name exists. <br>\n","To view the results in a bar plot, expand the cell below."]},{"cell_type":"code","execution_count":16,"id":"removed-express","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.execute_input":"2022-08-31T09:38:32.727875Z","iopub.status.busy":"2022-08-31T09:38:32.717848Z","iopub.status.idle":"2022-08-31T09:38:32.828008Z","shell.execute_reply":"2022-08-31T09:38:32.828407Z","shell.execute_reply.started":"2021-08-17T21:48:39.956069Z"},"papermill":{"duration":0.167267,"end_time":"2022-08-31T09:38:32.828552","exception":false,"start_time":"2022-08-31T09:38:32.661285","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Displaying via bar plot\n","sns.barplot(data=lastname_count.reset_index(), y='value_counts', x='index');\n","plt.xticks(rotation=45);"]},{"cell_type":"markdown","id":"regional-bermuda","metadata":{"papermill":{"duration":0.029015,"end_time":"2022-08-31T09:38:32.887143","exception":false,"start_time":"2022-08-31T09:38:32.858128","status":"completed"},"tags":[]},"source":["# Q12 - Define the people whom reserved a hotel with most number of babies and children."]},{"cell_type":"code","execution_count":17,"id":"inclusive-receptor","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:32.950583Z","iopub.status.busy":"2022-08-31T09:38:32.949856Z","iopub.status.idle":"2022-08-31T09:38:32.966524Z","shell.execute_reply":"2022-08-31T09:38:32.966959Z","shell.execute_reply.started":"2021-08-17T21:48:40.141317Z"},"papermill":{"duration":0.050484,"end_time":"2022-08-31T09:38:32.9671","exception":false,"start_time":"2022-08-31T09:38:32.916616","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Content</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>name</th>\n","      <td>Jamie Ramirez</td>\n","    </tr>\n","    <tr>\n","      <th>email</th>\n","      <td>Ramirez_Jamie22@aol.com</td>\n","    </tr>\n","    <tr>\n","      <th>phone-number</th>\n","      <td>268-823-5667</td>\n","    </tr>\n","    <tr>\n","      <th>total_babies_and_children</th>\n","      <td>10.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                           Content\n","name                                 Jamie Ramirez\n","email                      Ramirez_Jamie22@aol.com\n","phone-number                          268-823-5667\n","total_babies_and_children                     10.0"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["df['total_babies_and_children'] = df['babies'] + df['children'];\n","\n","df[['name','email','phone-number','total_babies_and_children']].loc[df['total_babies_and_children'].idxmax()].to_frame('Content')"]},{"cell_type":"markdown","id":"occupational-cooper","metadata":{"papermill":{"duration":0.030629,"end_time":"2022-08-31T09:38:33.027861","exception":false,"start_time":"2022-08-31T09:38:32.997232","status":"completed"},"tags":[]},"source":["Since the question wants the people with <u>most number of *babies* and *children*</u>, I summed the two columns *babies* and *children* together and stored them in a new column named *total_babies_and_children*.\n","\n","To show the people's informations, the index number of their record is needed. So, I used the *idxmax()* function to find the maximum result and give its index number. <br>\n","I putted the above result in *loc()* function to locate the specified record and then, display its other informations."]},{"cell_type":"markdown","id":"accessible-methodology","metadata":{"papermill":{"duration":0.029663,"end_time":"2022-08-31T09:38:33.089425","exception":false,"start_time":"2022-08-31T09:38:33.059762","status":"completed"},"tags":[]},"source":["# Q13 - Define the phone number of regions which had the most reservations."]},{"cell_type":"code","execution_count":18,"id":"functioning-greece","metadata":{"execution":{"iopub.execute_input":"2022-08-31T09:38:33.153969Z","iopub.status.busy":"2022-08-31T09:38:33.151525Z","iopub.status.idle":"2022-08-31T09:38:33.199718Z","shell.execute_reply":"2022-08-31T09:38:33.199257Z","shell.execute_reply.started":"2021-08-17T21:48:40.175451Z"},"papermill":{"duration":0.080374,"end_time":"2022-08-31T09:38:33.19983","exception":false,"start_time":"2022-08-31T09:38:33.119456","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Value counts</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>799</th>\n","      <td>168</td>\n","    </tr>\n","    <tr>\n","      <th>185</th>\n","      <td>167</td>\n","    </tr>\n","    <tr>\n","      <th>541</th>\n","      <td>166</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     Value counts\n","799           168\n","185           167\n","541           166"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["df['phone-number'].apply(lambda phone_no:phone_no[:3]).value_counts().nlargest(3).to_frame('Value counts')"]},{"cell_type":"markdown","id":"reverse-commission","metadata":{"papermill":{"duration":0.030548,"end_time":"2022-08-31T09:38:33.261294","exception":false,"start_time":"2022-08-31T09:38:33.230746","status":"completed"},"tags":[]},"source":["In order to get the regions national phone code, we need the first 3 digits of each number. To do this, I'd got the first 3 digits of each number via an inline function (lambda). Then, to count them, I used *value_counts()* function. <br>\n","Since the dataset's records are a lot, I just wanted to display the top 3 national phone codes with most reservations via *nlargest()* function.\n","\n","The *to_frame()* function is for displaying the results in a dataframe. The string in the function, is the name of the column."]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.7.10"},"papermill":{"default_parameters":{},"duration":13.728721,"end_time":"2022-08-31T09:38:34.708869","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2022-08-31T09:38:20.980148","version":"2.3.3"}},"nbformat":4,"nbformat_minor":5}