{ "cells": [ { "cell_type": "markdown", "id": "2eac3f7d-62ee-4545-9a96-b3d5aacc4da4", "metadata": {}, "source": [ "## Introduction to Data Science\n", "\n", "#### University of Redlands - DATA 101\n", "#### Prof: Joanna Bieri [joanna_bieri@redlands.edu](mailto:joanna_bieri@redlands.edu)\n", "#### [Class Website: data101.joannabieri.com](https://data101.joannabieri.com)\n", "\n", "---------------------------------------\n", "# Homework Day 6\n", "---------------------------------------\n", "\n", "GOALS:\n", "\n", "1. Answer all the questions from the lecture.\n", "2. Practice wrangling data - doing more advanced code\n", "3. Complete your first more advanced Exploration/Exercise\n", "\n", "----------------------------------------------------------\n", "\n", "This homework has **4 Questions** and **7 Exercises**\n", "\n", "NOTE:\n", "Exercises will tend to be an analysis of a single piece of data where one exercise builds on the next.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "85d851b3-4123-484c-a51a-561490abcc68", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:45.148872Z", "iopub.status.busy": "2026-09-09T23:21:45.148699Z", "iopub.status.idle": "2026-09-09T23:21:46.791815Z", "shell.execute_reply": "2026-09-09T23:21:46.791194Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "import plotly.express as px\n", "from plotly.subplots import make_subplots\n", "import plotly.io as pio\n", "pio.renderers.default = 'notebook_connected'\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "40945f6e-1a32-4c95-9a56-686a09db83f2", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:46.793553Z", "iopub.status.busy": "2026-09-09T23:21:46.793384Z", "iopub.status.idle": "2026-09-09T23:21:49.622065Z", "shell.execute_reply": "2026-09-09T23:21:49.621456Z" } }, "outputs": [], "source": [ "file_location = 'https://joannabieri.com/introdatascience/data/hotels.csv'\n", "DF_raw_hotels = pd.read_csv(file_location)\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "cf9bcb7c-2440-4651-aad0-d27e779933f6", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.623756Z", "iopub.status.busy": "2026-09-09T23:21:49.623580Z", "iopub.status.idle": "2026-09-09T23:21:49.664408Z", "shell.execute_reply": "2026-09-09T23:21:49.663859Z" } }, "outputs": [ { "data": { "text/html": [ "
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hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadults...deposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
0Resort Hotel03422015July271002...No DepositNaNNaN0Transient0.0000Check-Out2015-07-01
1Resort Hotel07372015July271002...No DepositNaNNaN0Transient0.0000Check-Out2015-07-01
2Resort Hotel072015July271011...No DepositNaNNaN0Transient75.0000Check-Out2015-07-02
3Resort Hotel0132015July271011...No Deposit304.0NaN0Transient75.0000Check-Out2015-07-02
4Resort Hotel0142015July271022...No Deposit240.0NaN0Transient98.0001Check-Out2015-07-03
..................................................................
119385City Hotel0232017August3530252...No Deposit394.0NaN0Transient96.1400Check-Out2017-09-06
119386City Hotel01022017August3531253...No Deposit9.0NaN0Transient225.4302Check-Out2017-09-07
119387City Hotel0342017August3531252...No Deposit9.0NaN0Transient157.7104Check-Out2017-09-07
119388City Hotel01092017August3531252...No Deposit89.0NaN0Transient104.4000Check-Out2017-09-07
119389City Hotel02052017August3529272...No Deposit9.0NaN0Transient151.2002Check-Out2017-09-07
\n", "

119390 rows × 32 columns

\n", "
" ], "text/plain": [ " hotel is_canceled lead_time arrival_date_year \\\n", "0 Resort Hotel 0 342 2015 \n", "1 Resort Hotel 0 737 2015 \n", "2 Resort Hotel 0 7 2015 \n", "3 Resort Hotel 0 13 2015 \n", "4 Resort Hotel 0 14 2015 \n", "... ... ... ... ... \n", "119385 City Hotel 0 23 2017 \n", "119386 City Hotel 0 102 2017 \n", "119387 City Hotel 0 34 2017 \n", "119388 City Hotel 0 109 2017 \n", "119389 City Hotel 0 205 2017 \n", "\n", " arrival_date_month arrival_date_week_number \\\n", "0 July 27 \n", "1 July 27 \n", "2 July 27 \n", "3 July 27 \n", "4 July 27 \n", "... ... ... \n", "119385 August 35 \n", "119386 August 35 \n", "119387 August 35 \n", "119388 August 35 \n", "119389 August 35 \n", "\n", " arrival_date_day_of_month stays_in_weekend_nights \\\n", "0 1 0 \n", "1 1 0 \n", "2 1 0 \n", "3 1 0 \n", "4 1 0 \n", "... ... ... \n", "119385 30 2 \n", "119386 31 2 \n", "119387 31 2 \n", "119388 31 2 \n", "119389 29 2 \n", "\n", " stays_in_week_nights adults ... deposit_type agent company \\\n", "0 0 2 ... No Deposit NaN NaN \n", "1 0 2 ... No Deposit NaN NaN \n", "2 1 1 ... No Deposit NaN NaN \n", "3 1 1 ... No Deposit 304.0 NaN \n", "4 2 2 ... No Deposit 240.0 NaN \n", "... ... ... ... ... ... ... \n", "119385 5 2 ... No Deposit 394.0 NaN \n", "119386 5 3 ... No Deposit 9.0 NaN \n", "119387 5 2 ... No Deposit 9.0 NaN \n", "119388 5 2 ... No Deposit 89.0 NaN \n", "119389 7 2 ... No Deposit 9.0 NaN \n", "\n", " days_in_waiting_list customer_type adr \\\n", "0 0 Transient 0.00 \n", "1 0 Transient 0.00 \n", "2 0 Transient 75.00 \n", "3 0 Transient 75.00 \n", "4 0 Transient 98.00 \n", "... ... ... ... \n", "119385 0 Transient 96.14 \n", "119386 0 Transient 225.43 \n", "119387 0 Transient 157.71 \n", "119388 0 Transient 104.40 \n", "119389 0 Transient 151.20 \n", "\n", " required_car_parking_spaces total_of_special_requests \\\n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 1 \n", "... ... ... \n", "119385 0 0 \n", "119386 0 2 \n", "119387 0 4 \n", "119388 0 0 \n", "119389 0 2 \n", "\n", " reservation_status reservation_status_date \n", "0 Check-Out 2015-07-01 \n", "1 Check-Out 2015-07-01 \n", "2 Check-Out 2015-07-02 \n", "3 Check-Out 2015-07-02 \n", "4 Check-Out 2015-07-03 \n", "... ... ... \n", "119385 Check-Out 2017-09-06 \n", "119386 Check-Out 2017-09-07 \n", "119387 Check-Out 2017-09-07 \n", "119388 Check-Out 2017-09-07 \n", "119389 Check-Out 2017-09-07 \n", "\n", "[119390 rows x 32 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(DF_raw_hotels)\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "2bee57b5-06b5-4502-bb8c-f9e5470126e5", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.666046Z", "iopub.status.busy": "2026-09-09T23:21:49.665872Z", "iopub.status.idle": "2026-09-09T23:21:49.668528Z", "shell.execute_reply": "2026-09-09T23:21:49.668021Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['hotel', 'is_canceled', 'lead_time', 'arrival_date_year', 'arrival_date_month', 'arrival_date_week_number', 'arrival_date_day_of_month', 'stays_in_weekend_nights', 'stays_in_week_nights', 'adults', 'children', 'babies', 'meal', 'country', 'market_segment', 'distribution_channel', 'is_repeated_guest', 'previous_cancellations', 'previous_bookings_not_canceled', 'reserved_room_type', 'assigned_room_type', 'booking_changes', 'deposit_type', 'agent', 'company', 'days_in_waiting_list', 'customer_type', 'adr', 'required_car_parking_spaces', 'total_of_special_requests', 'reservation_status', 'reservation_status_date']\n" ] } ], "source": [ "# I always look at the column names - they have to be spelled exactly!\n", "columns_list = list(DF_raw_hotels.keys())\n", "print(columns_list)\n" ] }, { "cell_type": "markdown", "id": "fb22b81a-8600-414d-aec8-5d8776ea6bde", "metadata": {}, "source": [ "### Example of Combining Masks\n", "\n", "* Show only results for visitors from USA or GBR." ] }, { "cell_type": "code", "execution_count": 5, "id": "e7ddf1a1-457b-4ba8-93f5-ad5e671e1700", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.670043Z", "iopub.status.busy": "2026-09-09T23:21:49.669884Z", "iopub.status.idle": "2026-09-09T23:21:49.701255Z", "shell.execute_reply": "2026-09-09T23:21:49.700742Z" } }, "outputs": [ { "data": { "text/html": [ "
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hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadults...deposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
2Resort Hotel072015July271011...No DepositNaNNaN0Transient75.0000Check-Out2015-07-02
3Resort Hotel0132015July271011...No Deposit304.0NaN0Transient75.0000Check-Out2015-07-02
4Resort Hotel0142015July271022...No Deposit240.0NaN0Transient98.0001Check-Out2015-07-03
5Resort Hotel0142015July271022...No Deposit240.0NaN0Transient98.0001Check-Out2015-07-03
12Resort Hotel0682015July271042...No Deposit240.0NaN0Transient97.0003Check-Out2015-07-05
..................................................................
119358City Hotel01352017August3530142...No Deposit14.0NaN0Transient143.1001Check-Out2017-09-04
119369City Hotel02072017August3425372...No Deposit14.0NaN0Transient96.2502Check-Out2017-09-04
119378City Hotel02472017August3531132...No Deposit42.0NaN0Transient86.8500Check-Out2017-09-04
119379City Hotel01092017August3531132...No Deposit9.0NaN0Transient155.0001Check-Out2017-09-04
119388City Hotel01092017August3531252...No Deposit89.0NaN0Transient104.4000Check-Out2017-09-07
\n", "

14226 rows × 32 columns

\n", "
" ], "text/plain": [ " hotel is_canceled lead_time arrival_date_year \\\n", "2 Resort Hotel 0 7 2015 \n", "3 Resort Hotel 0 13 2015 \n", "4 Resort Hotel 0 14 2015 \n", "5 Resort Hotel 0 14 2015 \n", "12 Resort Hotel 0 68 2015 \n", "... ... ... ... ... \n", "119358 City Hotel 0 135 2017 \n", "119369 City Hotel 0 207 2017 \n", "119378 City Hotel 0 247 2017 \n", "119379 City Hotel 0 109 2017 \n", "119388 City Hotel 0 109 2017 \n", "\n", " arrival_date_month arrival_date_week_number \\\n", "2 July 27 \n", "3 July 27 \n", "4 July 27 \n", "5 July 27 \n", "12 July 27 \n", "... ... ... \n", "119358 August 35 \n", "119369 August 34 \n", "119378 August 35 \n", "119379 August 35 \n", "119388 August 35 \n", "\n", " arrival_date_day_of_month stays_in_weekend_nights \\\n", "2 1 0 \n", "3 1 0 \n", "4 1 0 \n", "5 1 0 \n", "12 1 0 \n", "... ... ... \n", "119358 30 1 \n", "119369 25 3 \n", "119378 31 1 \n", "119379 31 1 \n", "119388 31 2 \n", "\n", " stays_in_week_nights adults ... deposit_type agent company \\\n", "2 1 1 ... No Deposit NaN NaN \n", "3 1 1 ... No Deposit 304.0 NaN \n", "4 2 2 ... No Deposit 240.0 NaN \n", "5 2 2 ... No Deposit 240.0 NaN \n", "12 4 2 ... No Deposit 240.0 NaN \n", "... ... ... ... ... ... ... \n", "119358 4 2 ... No Deposit 14.0 NaN \n", "119369 7 2 ... No Deposit 14.0 NaN \n", "119378 3 2 ... No Deposit 42.0 NaN \n", "119379 3 2 ... No Deposit 9.0 NaN \n", "119388 5 2 ... No Deposit 89.0 NaN \n", "\n", " days_in_waiting_list customer_type adr \\\n", "2 0 Transient 75.00 \n", "3 0 Transient 75.00 \n", "4 0 Transient 98.00 \n", "5 0 Transient 98.00 \n", "12 0 Transient 97.00 \n", "... ... ... ... \n", "119358 0 Transient 143.10 \n", "119369 0 Transient 96.25 \n", "119378 0 Transient 86.85 \n", "119379 0 Transient 155.00 \n", "119388 0 Transient 104.40 \n", "\n", " required_car_parking_spaces total_of_special_requests \\\n", "2 0 0 \n", "3 0 0 \n", "4 0 1 \n", "5 0 1 \n", "12 0 3 \n", "... ... ... \n", "119358 0 1 \n", "119369 0 2 \n", "119378 0 0 \n", "119379 0 1 \n", "119388 0 0 \n", "\n", " reservation_status reservation_status_date \n", "2 Check-Out 2015-07-02 \n", "3 Check-Out 2015-07-02 \n", "4 Check-Out 2015-07-03 \n", "5 Check-Out 2015-07-03 \n", "12 Check-Out 2015-07-05 \n", "... ... ... \n", "119358 Check-Out 2017-09-04 \n", "119369 Check-Out 2017-09-04 \n", "119378 Check-Out 2017-09-04 \n", "119379 Check-Out 2017-09-04 \n", "119388 Check-Out 2017-09-07 \n", "\n", "[14226 rows x 32 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mask = (DF_raw_hotels['country']=='USA') | (DF_raw_hotels['country']=='GBR')\n", "DF_raw_hotels[mask]\n" ] }, { "cell_type": "markdown", "id": "375f4067-5efd-40c4-991f-6545c3f263ea", "metadata": {}, "source": [ "## Example Finding unique values\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "ac44ea21-a1ec-4de5-9f42-fdfc5cb5a28c", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.702858Z", "iopub.status.busy": "2026-09-09T23:21:49.702693Z", "iopub.status.idle": "2026-09-09T23:21:49.720771Z", "shell.execute_reply": "2026-09-09T23:21:49.720293Z" } }, "outputs": [ { "data": { "text/html": [ "
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market_segmentcustomer_type
0DirectTransient
3CorporateTransient
4Online TATransient
9Offline TA/TOTransient
16Offline TA/TOContract
47Offline TA/TOTransient-Party
125ComplementaryTransient
127Online TATransient-Party
260DirectTransient-Party
413GroupsTransient-Party
417GroupsTransient
539Online TAGroup
1530CorporateTransient-Party
1539DirectGroup
1587Offline TA/TOGroup
2262CorporateContract
2263DirectContract
2698GroupsContract
9511ComplementaryTransient-Party
14051ComplementaryGroup
14654GroupsGroup
15141CorporateGroup
15261ComplementaryContract
15506Online TAContract
40600UndefinedTransient-Party
49013AviationTransient-Party
49372AviationTransient
112102AviationGroup
\n", "
" ], "text/plain": [ " market_segment customer_type\n", "0 Direct Transient\n", "3 Corporate Transient\n", "4 Online TA Transient\n", "9 Offline TA/TO Transient\n", "16 Offline TA/TO Contract\n", "47 Offline TA/TO Transient-Party\n", "125 Complementary Transient\n", "127 Online TA Transient-Party\n", "260 Direct Transient-Party\n", "413 Groups Transient-Party\n", "417 Groups Transient\n", "539 Online TA Group\n", "1530 Corporate Transient-Party\n", "1539 Direct Group\n", "1587 Offline TA/TO Group\n", "2262 Corporate Contract\n", "2263 Direct Contract\n", "2698 Groups Contract\n", "9511 Complementary Transient-Party\n", "14051 Complementary Group\n", "14654 Groups Group\n", "15141 Corporate Group\n", "15261 Complementary Contract\n", "15506 Online TA Contract\n", "40600 Undefined Transient-Party\n", "49013 Aviation Transient-Party\n", "49372 Aviation Transient\n", "112102 Aviation Group" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_columns = ['market_segment','customer_type']\n", "DF_raw_hotels[my_columns].drop_duplicates()\n" ] }, { "cell_type": "markdown", "id": "fd184c04-061b-434d-ba4a-9460c5ceb25c", "metadata": {}, "source": [ "## Example Counting unique values\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "30dd04ac-3834-4805-ba5f-2b7f87d1ec9e", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.722340Z", "iopub.status.busy": "2026-09-09T23:21:49.722175Z", "iopub.status.idle": "2026-09-09T23:21:49.732737Z", "shell.execute_reply": "2026-09-09T23:21:49.732388Z" } }, "outputs": [ { "data": { "text/plain": [ "market_segment\n", "Online TA 56477\n", "Offline TA/TO 24219\n", "Groups 19811\n", "Direct 12606\n", "Corporate 5295\n", "Complementary 743\n", "Aviation 237\n", "Undefined 2\n", "Name: count, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_columns = ['market_segment']\n", "DF_raw_hotels[my_columns].value_counts()\n" ] }, { "cell_type": "markdown", "id": "f1593bee-5c60-4538-9fa9-12527fc6d2ea", "metadata": {}, "source": [ "## Example of Counting unique values in two columns - unsorted\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "2e61352f-d003-4879-b60a-dde3f6c19751", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.734224Z", "iopub.status.busy": "2026-09-09T23:21:49.734070Z", "iopub.status.idle": "2026-09-09T23:21:49.751411Z", "shell.execute_reply": "2026-09-09T23:21:49.750993Z" } }, "outputs": [ { "data": { "text/plain": [ "market_segment customer_type \n", "Aviation Group 2\n", " Transient 218\n", " Transient-Party 17\n", "Complementary Contract 2\n", " Group 6\n", " Transient 703\n", " Transient-Party 32\n", "Corporate Contract 22\n", " Group 29\n", " Transient 3576\n", " Transient-Party 1668\n", "Direct Contract 14\n", " Group 134\n", " Transient 11336\n", " Transient-Party 1122\n", "Groups Contract 735\n", " Group 16\n", " Transient 8427\n", " Transient-Party 10633\n", "Offline TA/TO Contract 1817\n", " Group 211\n", " Transient 14054\n", " Transient-Party 8137\n", "Online TA Contract 1486\n", " Group 179\n", " Transient 51299\n", " Transient-Party 3513\n", "Undefined Transient-Party 2\n", "Name: count, dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_columns = ['market_segment','customer_type']\n", "DF_raw_hotels[my_columns].value_counts(sort=False)\n" ] }, { "cell_type": "markdown", "id": "c2350169-ed73-4089-8fed-9484ec05e923", "metadata": {}, "source": [ "## Example of Saving Unique Values to a Data Frame\n", "\n", "Just add the command **.reset_index().rename(columns={\"index\": \"value\", 0: \"count\"})**. In this class " ] }, { "cell_type": "code", "execution_count": 9, "id": "a1b8e743-cc66-4290-97fa-2a7cad2bb510", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.752961Z", "iopub.status.busy": "2026-09-09T23:21:49.752804Z", "iopub.status.idle": "2026-09-09T23:21:49.772929Z", "shell.execute_reply": "2026-09-09T23:21:49.772395Z" } }, "outputs": [ { "data": { "text/html": [ "
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market_segmentcustomer_typecount
0Online TATransient51299
1Offline TA/TOTransient14054
2DirectTransient11336
3GroupsTransient-Party10633
4GroupsTransient8427
5Offline TA/TOTransient-Party8137
6CorporateTransient3576
7Online TATransient-Party3513
8Offline TA/TOContract1817
9CorporateTransient-Party1668
10Online TAContract1486
11DirectTransient-Party1122
12GroupsContract735
13ComplementaryTransient703
14AviationTransient218
15Offline TA/TOGroup211
16Online TAGroup179
17DirectGroup134
18ComplementaryTransient-Party32
19CorporateGroup29
20CorporateContract22
21AviationTransient-Party17
22GroupsGroup16
23DirectContract14
24ComplementaryGroup6
25ComplementaryContract2
26AviationGroup2
27UndefinedTransient-Party2
\n", "
" ], "text/plain": [ " market_segment customer_type count\n", "0 Online TA Transient 51299\n", "1 Offline TA/TO Transient 14054\n", "2 Direct Transient 11336\n", "3 Groups Transient-Party 10633\n", "4 Groups Transient 8427\n", "5 Offline TA/TO Transient-Party 8137\n", "6 Corporate Transient 3576\n", "7 Online TA Transient-Party 3513\n", "8 Offline TA/TO Contract 1817\n", "9 Corporate Transient-Party 1668\n", "10 Online TA Contract 1486\n", "11 Direct Transient-Party 1122\n", "12 Groups Contract 735\n", "13 Complementary Transient 703\n", "14 Aviation Transient 218\n", "15 Offline TA/TO Group 211\n", "16 Online TA Group 179\n", "17 Direct Group 134\n", "18 Complementary Transient-Party 32\n", "19 Corporate Group 29\n", "20 Corporate Contract 22\n", "21 Aviation Transient-Party 17\n", "22 Groups Group 16\n", "23 Direct Contract 14\n", "24 Complementary Group 6\n", "25 Complementary Contract 2\n", "26 Aviation Group 2\n", "27 Undefined Transient-Party 2" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "my_columns = ['market_segment','customer_type']\n", "my_counts = DF_raw_hotels[my_columns].value_counts()\n", "my_counts = my_counts.reset_index().rename(columns={\"index\": \"value\", 0: \"count\"})\n", "display(my_counts)\n" ] }, { "cell_type": "markdown", "id": "4113401c-1059-464c-bc4f-6787bf224f50", "metadata": {}, "source": [ "## Example of Adding a column to a Data Frame\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "ace7c62a-4085-43bb-b4d0-677f39bfa9f1", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.774507Z", "iopub.status.busy": "2026-09-09T23:21:49.774346Z", "iopub.status.idle": "2026-09-09T23:21:49.810121Z", "shell.execute_reply": "2026-09-09T23:21:49.809681Z" } }, "outputs": [ { "data": { "text/html": [ "
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hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadults...agentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datelittle_ones
0Resort Hotel03422015July271002...NaNNaN0Transient0.0000Check-Out2015-07-010.0
1Resort Hotel07372015July271002...NaNNaN0Transient0.0000Check-Out2015-07-010.0
2Resort Hotel072015July271011...NaNNaN0Transient75.0000Check-Out2015-07-020.0
3Resort Hotel0132015July271011...304.0NaN0Transient75.0000Check-Out2015-07-020.0
4Resort Hotel0142015July271022...240.0NaN0Transient98.0001Check-Out2015-07-030.0
..................................................................
119385City Hotel0232017August3530252...394.0NaN0Transient96.1400Check-Out2017-09-060.0
119386City Hotel01022017August3531253...9.0NaN0Transient225.4302Check-Out2017-09-070.0
119387City Hotel0342017August3531252...9.0NaN0Transient157.7104Check-Out2017-09-070.0
119388City Hotel01092017August3531252...89.0NaN0Transient104.4000Check-Out2017-09-070.0
119389City Hotel02052017August3529272...9.0NaN0Transient151.2002Check-Out2017-09-070.0
\n", "

119390 rows × 33 columns

\n", "
" ], "text/plain": [ " hotel is_canceled lead_time arrival_date_year \\\n", "0 Resort Hotel 0 342 2015 \n", "1 Resort Hotel 0 737 2015 \n", "2 Resort Hotel 0 7 2015 \n", "3 Resort Hotel 0 13 2015 \n", "4 Resort Hotel 0 14 2015 \n", "... ... ... ... ... \n", "119385 City Hotel 0 23 2017 \n", "119386 City Hotel 0 102 2017 \n", "119387 City Hotel 0 34 2017 \n", "119388 City Hotel 0 109 2017 \n", "119389 City Hotel 0 205 2017 \n", "\n", " arrival_date_month arrival_date_week_number \\\n", "0 July 27 \n", "1 July 27 \n", "2 July 27 \n", "3 July 27 \n", "4 July 27 \n", "... ... ... \n", "119385 August 35 \n", "119386 August 35 \n", "119387 August 35 \n", "119388 August 35 \n", "119389 August 35 \n", "\n", " arrival_date_day_of_month stays_in_weekend_nights \\\n", "0 1 0 \n", "1 1 0 \n", "2 1 0 \n", "3 1 0 \n", "4 1 0 \n", "... ... ... \n", "119385 30 2 \n", "119386 31 2 \n", "119387 31 2 \n", "119388 31 2 \n", "119389 29 2 \n", "\n", " stays_in_week_nights adults ... agent company \\\n", "0 0 2 ... NaN NaN \n", "1 0 2 ... NaN NaN \n", "2 1 1 ... NaN NaN \n", "3 1 1 ... 304.0 NaN \n", "4 2 2 ... 240.0 NaN \n", "... ... ... ... ... ... \n", "119385 5 2 ... 394.0 NaN \n", "119386 5 3 ... 9.0 NaN \n", "119387 5 2 ... 9.0 NaN \n", "119388 5 2 ... 89.0 NaN \n", "119389 7 2 ... 9.0 NaN \n", "\n", " days_in_waiting_list customer_type adr required_car_parking_spaces \\\n", "0 0 Transient 0.00 0 \n", "1 0 Transient 0.00 0 \n", "2 0 Transient 75.00 0 \n", "3 0 Transient 75.00 0 \n", "4 0 Transient 98.00 0 \n", "... ... ... ... ... \n", "119385 0 Transient 96.14 0 \n", "119386 0 Transient 225.43 0 \n", "119387 0 Transient 157.71 0 \n", "119388 0 Transient 104.40 0 \n", "119389 0 Transient 151.20 0 \n", "\n", " total_of_special_requests reservation_status \\\n", "0 0 Check-Out \n", "1 0 Check-Out \n", "2 0 Check-Out \n", "3 0 Check-Out \n", "4 1 Check-Out \n", "... ... ... \n", "119385 0 Check-Out \n", "119386 2 Check-Out \n", "119387 4 Check-Out \n", "119388 0 Check-Out \n", "119389 2 Check-Out \n", "\n", " reservation_status_date little_ones \n", "0 2015-07-01 0.0 \n", "1 2015-07-01 0.0 \n", "2 2015-07-02 0.0 \n", "3 2015-07-02 0.0 \n", "4 2015-07-03 0.0 \n", "... ... ... \n", "119385 2017-09-06 0.0 \n", "119386 2017-09-07 0.0 \n", "119387 2017-09-07 0.0 \n", "119388 2017-09-07 0.0 \n", "119389 2017-09-07 0.0 \n", "\n", "[119390 rows x 33 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DF_raw_hotels['little_ones'] = DF_raw_hotels['children'] + DF_raw_hotels['babies']\n", "DF_raw_hotels\n" ] }, { "cell_type": "markdown", "id": "d7ce457a-38c5-48b2-97ee-eecc6af9bf0e", "metadata": {}, "source": [ "**Q1** Count up the number of little_ones in different hotel types (Hint - use value_counts() with hotel and little_ones as your columns.)\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "342b5502-ab88-49d0-9dcb-862a7f8602b8", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.811798Z", "iopub.status.busy": "2026-09-09T23:21:49.811625Z", "iopub.status.idle": "2026-09-09T23:21:49.813737Z", "shell.execute_reply": "2026-09-09T23:21:49.813332Z" } }, "outputs": [], "source": [ "# Your code here:\n" ] }, { "cell_type": "markdown", "id": "3b1b52ae-36e1-49c1-bd1b-30165cbfd0ce", "metadata": {}, "source": [ "**Q2** Add a new column that calculates the total number of humans (all_humans) in the room (adults+children+babies) (Hint - you can copy and paste the code above where we calculated little_ones and add on another thing)" ] }, { "cell_type": "code", "execution_count": 12, "id": "70ba2273-7186-46c1-8e4d-87bdaa7f6b78", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.815223Z", "iopub.status.busy": "2026-09-09T23:21:49.815075Z", "iopub.status.idle": "2026-09-09T23:21:49.817019Z", "shell.execute_reply": "2026-09-09T23:21:49.816609Z" } }, "outputs": [], "source": [ "# Your code here\n" ] }, { "cell_type": "markdown", "id": "41bea042-6828-4b52-a156-82e7b7ccf21e", "metadata": {}, "source": [ "**Q3** Can you figure out what the code below is doing - comment on each line, remember you can add a comment using the hashtag #" ] }, { "cell_type": "code", "execution_count": 13, "id": "353b836e-d2ab-472d-a483-466ca5d08178", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.818473Z", "iopub.status.busy": "2026-09-09T23:21:49.818326Z", "iopub.status.idle": "2026-09-09T23:21:49.831985Z", "shell.execute_reply": "2026-09-09T23:21:49.831485Z" } }, "outputs": [], "source": [ "# PLEASE ADD YOUR COMMENTS!\n", "DF_raw_hotels['little_ones'] = DF_raw_hotels['children'] + DF_raw_hotels['babies']\n", "my_columns = ['hotel','little_ones']\n", "my_counts = DF_raw_hotels[my_columns].value_counts()\n", "my_counts = my_counts.reset_index().rename(columns={\"index\": \"value\", 0: \"count\"})\n", "my_counts['proportion']=my_counts['count']/sum(my_counts['count'])\n" ] }, { "cell_type": "markdown", "id": "cee2cacf-ced0-4919-8365-4f9dc747a190", "metadata": {}, "source": [ "## Example of Grouping the data frame into chunks\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "40f5b055-efcf-42ef-a2c2-9cd5e4dcf37e", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.833523Z", "iopub.status.busy": "2026-09-09T23:21:49.833369Z", "iopub.status.idle": "2026-09-09T23:21:49.846924Z", "shell.execute_reply": "2026-09-09T23:21:49.846571Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
adultschildrenlittle_onesbabies
hotel
City Hotel1468387248.07640.0392
Resort Hotel747985155.05712.0557
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" ], "text/plain": [ " adults children little_ones babies\n", "hotel \n", "City Hotel 146838 7248.0 7640.0 392\n", "Resort Hotel 74798 5155.0 5712.0 557" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_columns = ['adults','children','little_ones','babies']\n", "DF_raw_hotels.groupby(by=['hotel'])[my_columns].sum()\n" ] }, { "cell_type": "markdown", "id": "81237655-f899-49ee-a158-2fac73e8441e", "metadata": {}, "source": [ "There are lots of different operations you could use:\n", "\n", "* .min()\n", "* .max()\n", "* .mean()\n", "* .median()\n", "* .sum()\n", "* .prod()\n", "* .count()\n", "* .describe()\n" ] }, { "cell_type": "markdown", "id": "8caf0e2c-79db-49d1-8869-cc69aaf611e6", "metadata": {}, "source": [ "Here we see that .describe() does all the descriptive statistics for each of columns, broken into the two groups. \n", "\n", "## Example of Grouping to quickly generate data\n", "\n", "The .groupby() function lets you quickly generate data about things you care about. In just one command below we are able to look at the total number of occupants for each of the columns (adults, children, little_ones, and babies) for each month in the data set." ] }, { "cell_type": "code", "execution_count": 15, "id": "58542d27-8f84-4e03-b7e4-733c17e5b492", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.848466Z", "iopub.status.busy": "2026-09-09T23:21:49.848308Z", "iopub.status.idle": "2026-09-09T23:21:49.862659Z", "shell.execute_reply": "2026-09-09T23:21:49.862245Z" } }, "outputs": [ { "data": { "text/html": [ "
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adultschildrenlittle_onesbabies
arrival_date_month
April208061141.01194.053
August277952780.02976.0196
December12382736.0814.078
February14450790.0849.059
January10024452.0500.048
July251642322.02443.0121
June203531057.01128.071
March17675700.0757.057
May21539845.0917.072
November11488279.0324.045
October20279703.0765.062
September19681598.0685.087
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" ], "text/plain": [ " adults children little_ones babies\n", "arrival_date_month \n", "April 20806 1141.0 1194.0 53\n", "August 27795 2780.0 2976.0 196\n", "December 12382 736.0 814.0 78\n", "February 14450 790.0 849.0 59\n", "January 10024 452.0 500.0 48\n", "July 25164 2322.0 2443.0 121\n", "June 20353 1057.0 1128.0 71\n", "March 17675 700.0 757.0 57\n", "May 21539 845.0 917.0 72\n", "November 11488 279.0 324.0 45\n", "October 20279 703.0 765.0 62\n", "September 19681 598.0 685.0 87" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DF_raw_hotels.groupby(by=['arrival_date_month'])[my_columns].sum()\n" ] }, { "cell_type": "markdown", "id": "40bdff93-753e-4212-bc44-4c2bbab99e86", "metadata": {}, "source": [ "**Q4** Try changing the code above to calculate the mean, max, and min for the above data." ] }, { "cell_type": "markdown", "id": "12cdd392-d3d8-4252-ae65-3b5d0a74dd8d", "metadata": {}, "source": [ "# Exercises:" ] }, { "cell_type": "markdown", "id": "91449490-f20d-4395-96ed-7f1eca0dd943", "metadata": {}, "source": [ "## Our first big-ish problem:\n", "\n", "Now you will try to use our hotels data set to answer some questions! You will need to use the tools we have learned so far to look into the data and try to answer the questions. Really try to answer the questions with as few hints as possible!\n", "\n", "### IF YOU USE MY ANSWER - avoid plagiarism\n", "\n", "To avoid plagiarism, if you end up copying my answer and using it, YOU MUST explain clearly what each part of the code is doing. It's okay to need to use my code at this point, but it is not helpful to your learning to blindly copy and paste without trying to understand.\n", "\n", "**Hints and Answers can be found in the lecture notes!**\n", "\n", "### Exercise 1\n", "\n", "Are people traveling on a whim?\n", "\n", "How could you find out?\n", "\n", "Try creating a mask filtering for hotel bookings where the guest is **not** from the US (`country` code `\"USA\"`) and the `lead_time` is less than 1 day.\n", "\n", "What proportion of people traveled last minute?\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "bec396a5-58cc-4f0d-afb0-7c6a3f5412e3", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.864334Z", "iopub.status.busy": "2026-09-09T23:21:49.864172Z", "iopub.status.idle": "2026-09-09T23:21:49.866367Z", "shell.execute_reply": "2026-09-09T23:21:49.865917Z" } }, "outputs": [], "source": [ "# Your code here\n" ] }, { "cell_type": "markdown", "id": "6ff3060b-6bf7-4580-ac3d-4e78b8d20ff5", "metadata": {}, "source": [ "### Exercise 2\n", "\n", "How many bookings involve at least 1 child **or** baby?" ] }, { "cell_type": "code", "execution_count": 17, "id": "9078b52e-a69d-4ede-ba0d-7851eea2c0a2", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.867922Z", "iopub.status.busy": "2026-09-09T23:21:49.867710Z", "iopub.status.idle": "2026-09-09T23:21:49.869586Z", "shell.execute_reply": "2026-09-09T23:21:49.869247Z" } }, "outputs": [], "source": [ "# Your code here:\n" ] }, { "cell_type": "markdown", "id": "11fb134b-4ff6-4d33-b8a9-b19f6d502f36", "metadata": {}, "source": [ "### Exercise 3\n", "\n", "Do you think it's more likely to find bookings with children or babies in city hotels or resort hotels?\n", "Test your intuition.\n" ] }, { "cell_type": "code", "execution_count": 18, "id": "c57b1e59-67e1-48fd-be59-06a6f4909ee5", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.871070Z", "iopub.status.busy": "2026-09-09T23:21:49.870918Z", "iopub.status.idle": "2026-09-09T23:21:49.872741Z", "shell.execute_reply": "2026-09-09T23:21:49.872402Z" } }, "outputs": [], "source": [ "# Your code here:\n" ] }, { "cell_type": "markdown", "id": "80587774-3bfc-400d-b2b1-4e8188f8df4b", "metadata": {}, "source": [ "### Exercise 4\n", "\n", "Create a frequency table of the number of `adults` in a booking.\n", "Display the results in descending order so the most common observation is on top.\n", "What is the most common number of adults in bookings in this dataset?\n", "Are there any surprising results?\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "bd513df5-928a-48f8-b940-9edd18ff9d04", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.874191Z", "iopub.status.busy": "2026-09-09T23:21:49.874045Z", "iopub.status.idle": "2026-09-09T23:21:49.875795Z", "shell.execute_reply": "2026-09-09T23:21:49.875473Z" } }, "outputs": [], "source": [ "# Your code here:\n" ] }, { "cell_type": "markdown", "id": "d38741ad-d89a-469e-b528-995aab4b2027", "metadata": {}, "source": [ "### Exercise 5\n", "\n", "Repeat Exercise 4, once for canceled bookings (`is_canceled` coded as 1) and once for not canceled bookings (`is_canceled` coded as 0).\n", "What does this reveal about the surprising results you spotted in the previous exercise?" ] }, { "cell_type": "code", "execution_count": 20, "id": "888b434d-6d09-466b-bc49-9f52a5dc8fad", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.877277Z", "iopub.status.busy": "2026-09-09T23:21:49.877131Z", "iopub.status.idle": "2026-09-09T23:21:49.878893Z", "shell.execute_reply": "2026-09-09T23:21:49.878565Z" } }, "outputs": [], "source": [ "# Your code here:\n" ] }, { "cell_type": "markdown", "id": "d6b87569-1f06-417f-916c-3cfc1de5739d", "metadata": {}, "source": [ "### Exercise 6\n", "\n", "Calculate minimum, mean, median, and maximum average daily rate (column labeled 'adr') grouped by `hotel` type so that you can get these statistics separately for resort and city hotels. (Hint - try using .describe() after doing group by)\n", "\n", "Which type of hotel is higher, on average?" ] }, { "cell_type": "code", "execution_count": 21, "id": "3e504f44-a5ac-45a9-8027-3d3fd8b80988", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.880359Z", "iopub.status.busy": "2026-09-09T23:21:49.880214Z", "iopub.status.idle": "2026-09-09T23:21:49.881959Z", "shell.execute_reply": "2026-09-09T23:21:49.881633Z" } }, "outputs": [], "source": [ "# Your code here:\n" ] }, { "cell_type": "markdown", "id": "11274118-dba5-44aa-b7fc-3b0fcdbe024d", "metadata": {}, "source": [ "### Exercise 7\n", "\n", "We observe two unusual values in the summary statistics above: a negative minimum, and a very high maximum.\n", "What types of hotels are these?\n", "\n", "Locate these observations in the data set and find out the arrival date (year and month) as well as how many people (adults, children, and babies) stayed in the room.\n", "\n", "You can investigate the data by showing the data frame to locate these values, but preferably you should identify them in a reproducible way with some code.\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "14fb08c9-e48e-4745-a051-a95aeb788014", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:21:49.883366Z", "iopub.status.busy": "2026-09-09T23:21:49.883219Z", "iopub.status.idle": "2026-09-09T23:21:49.884990Z", "shell.execute_reply": "2026-09-09T23:21:49.884648Z" } }, "outputs": [], "source": [ "# Your code here: \n" ] } ], "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 }