{ "cells": [ { "cell_type": "markdown", "id": "ee554bcf-d385-4a3d-8e06-90ca8141ffd2", "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 5\n", "---------------------------------------\n", "\n", "GOALS:\n", "\n", "1. Answer all the questions from the lecture.\n", "2. Practice wrangling data\n", "3. (challenge) do a mini data analysis of your own!\n", "\n", "----------------------------------------------------------\n", "\n", "This homework has **7 questions** from the lecture and **2 Problems**.\n", "\n", "NOTE:\n", "* Questions tend to be short answer or things were you change very minor parts of some given code.\n", "* Problems tend to be more involved, like trying out our methods on a new column.\n", "* Challenge problems are optional! They are intended to challenge you to reach beyond the basics of the class. I hope you will try the challenge problems!" ] }, { "cell_type": "code", "execution_count": 1, "id": "85d851b3-4123-484c-a51a-561490abcc68", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:07.108521Z", "iopub.status.busy": "2026-09-09T23:01:07.107953Z", "iopub.status.idle": "2026-09-09T23:01:08.827958Z", "shell.execute_reply": "2026-09-09T23:01:08.827252Z" } }, "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'" ] }, { "cell_type": "markdown", "id": "69e2e95d-f2b6-478a-8abf-199182396a99", "metadata": {}, "source": [ "### Load the raw data:\n", "\n", "* Data from two hotels: one resort and one city hotel\n", "* Observations: Each row represents a hotel booking\n", "* Goal for original data collection: Development of prediction models to classify a hotel booking's likelihood to be canceled [Antonia et al., 2019](https://www.sciencedirect.com/science/article/pii/S2352340918315191#bib5)\n", "\n", "\n", "[Data Information: Tidy Tuesday](https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-02-11/readme.md)" ] }, { "cell_type": "code", "execution_count": 2, "id": "40945f6e-1a32-4c95-9a56-686a09db83f2", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:08.829744Z", "iopub.status.busy": "2026-09-09T23:01:08.829566Z", "iopub.status.idle": "2026-09-09T23:01:11.771591Z", "shell.execute_reply": "2026-09-09T23:01:11.771084Z" } }, "outputs": [], "source": [ "file_location = 'https://joannabieri.com/introdatascience/data/hotels.csv'\n", "DF_raw_hotels = pd.read_csv(file_location)" ] }, { "cell_type": "code", "execution_count": 3, "id": "cf9bcb7c-2440-4651-aad0-d27e779933f6", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.773283Z", "iopub.status.busy": "2026-09-09T23:01:11.773112Z", "iopub.status.idle": "2026-09-09T23:01:11.814012Z", "shell.execute_reply": "2026-09-09T23:01:11.813439Z" } }, "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)" ] }, { "cell_type": "markdown", "id": "f359fa7d-4856-4fcd-8e18-fcf93dd9d0be", "metadata": {}, "source": [ "**Q1** Can you figure out how you would focus on multiple columns? We have done this before so see if you can remember the command. It's okay if you need to copy and paste!" ] }, { "cell_type": "code", "execution_count": 4, "id": "91f08a89-353d-454a-8708-c9c5e50afb34", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.815668Z", "iopub.status.busy": "2026-09-09T23:01:11.815489Z", "iopub.status.idle": "2026-09-09T23:01:11.817754Z", "shell.execute_reply": "2026-09-09T23:01:11.817247Z" } }, "outputs": [], "source": [ "# Your code here\n" ] }, { "cell_type": "markdown", "id": "ca5017c2-75b6-44e9-b71c-85f65c2bc4f6", "metadata": {}, "source": [ "#### Sorting Example - Descending order" ] }, { "cell_type": "code", "execution_count": 5, "id": "1c4d7619-53d6-4de3-8f5b-578e080294d7", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.819284Z", "iopub.status.busy": "2026-09-09T23:01:11.819123Z", "iopub.status.idle": "2026-09-09T23:01:11.832519Z", "shell.execute_reply": "2026-09-09T23:01:11.832100Z" } }, "outputs": [ { "data": { "text/html": [ "
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hotellead_time
1Resort Hotel737
4182Resort Hotel709
65241City Hotel629
65240City Hotel629
65242City Hotel629
.........
17976Resort Hotel0
18001Resort Hotel0
17989Resort Hotel0
28495Resort Hotel0
28493Resort Hotel0
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119390 rows × 2 columns

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" ], "text/plain": [ " hotel lead_time\n", "1 Resort Hotel 737\n", "4182 Resort Hotel 709\n", "65241 City Hotel 629\n", "65240 City Hotel 629\n", "65242 City Hotel 629\n", "... ... ...\n", "17976 Resort Hotel 0\n", "18001 Resort Hotel 0\n", "17989 Resort Hotel 0\n", "28495 Resort Hotel 0\n", "28493 Resort Hotel 0\n", "\n", "[119390 rows x 2 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "my_columns = ['hotel','lead_time']\n", "DF_lead_time_sorted = DF_raw_hotels[my_columns].sort_values('lead_time', ascending=False)\n", "display(DF_lead_time_sorted)" ] }, { "cell_type": "markdown", "id": "18d33eb8-3d1b-4e86-8323-bbae51fc9567", "metadata": {}, "source": [ "**Q2** Choose another set of variables (columns) that you can explore. Try sorting them ascending and descending." ] }, { "cell_type": "code", "execution_count": 6, "id": "7ef50625-57ec-42ac-9916-ae672f00878e", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.834130Z", "iopub.status.busy": "2026-09-09T23:01:11.833959Z", "iopub.status.idle": "2026-09-09T23:01:11.836163Z", "shell.execute_reply": "2026-09-09T23:01:11.835728Z" } }, "outputs": [], "source": [ "# Your code here:\n" ] }, { "cell_type": "markdown", "id": "40947dba-cf3a-442e-b760-7502cd4ad9f0", "metadata": {}, "source": [ "**Q3** What would happen if you tried to sort categorical data? Try sorting by the column \"arrival_date_month\". Do you get an error? Does it sort it? If so how is it sorted?" ] }, { "cell_type": "code", "execution_count": 7, "id": "b58b40e4-009d-406d-882f-86ce5465d9d1", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.837600Z", "iopub.status.busy": "2026-09-09T23:01:11.837439Z", "iopub.status.idle": "2026-09-09T23:01:11.839512Z", "shell.execute_reply": "2026-09-09T23:01:11.839115Z" } }, "outputs": [], "source": [ "# Your code here:\n" ] }, { "cell_type": "markdown", "id": "93356ebc-6ed4-488a-a902-a7a505ed299b", "metadata": {}, "source": [ "### Boolean Mask Example - Subselect Data\n", "\n", "Here is an example of focusing only on 'Resort Hotel' data in the 'hotel' column" ] }, { "cell_type": "code", "execution_count": 8, "id": "dff47264-5478-429e-8600-bb2c6fcfaf31", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.840943Z", "iopub.status.busy": "2026-09-09T23:01:11.840785Z", "iopub.status.idle": "2026-09-09T23:01:11.872560Z", "shell.execute_reply": "2026-09-09T23:01:11.872162Z" } }, "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
..................................................................
40055Resort Hotel02122017August3531282...No Deposit143.0NaN0Transient89.7500Check-Out2017-09-10
40056Resort Hotel01692017August3530292...No Deposit250.0NaN0Transient-Party202.2701Check-Out2017-09-10
40057Resort Hotel02042017August35294102...No Deposit250.0NaN0Transient153.5703Check-Out2017-09-12
40058Resort Hotel02112017August35314102...No Deposit40.0NaN0Contract112.8001Check-Out2017-09-14
40059Resort Hotel01612017August35314102...No Deposit69.0NaN0Transient99.0600Check-Out2017-09-14
\n", "

40060 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", "40055 Resort Hotel 0 212 2017 \n", "40056 Resort Hotel 0 169 2017 \n", "40057 Resort Hotel 0 204 2017 \n", "40058 Resort Hotel 0 211 2017 \n", "40059 Resort Hotel 0 161 2017 \n", "\n", " arrival_date_month arrival_date_week_number arrival_date_day_of_month \\\n", "0 July 27 1 \n", "1 July 27 1 \n", "2 July 27 1 \n", "3 July 27 1 \n", "4 July 27 1 \n", "... ... ... ... \n", "40055 August 35 31 \n", "40056 August 35 30 \n", "40057 August 35 29 \n", "40058 August 35 31 \n", "40059 August 35 31 \n", "\n", " stays_in_weekend_nights stays_in_week_nights adults ... \\\n", "0 0 0 2 ... \n", "1 0 0 2 ... \n", "2 0 1 1 ... \n", "3 0 1 1 ... \n", "4 0 2 2 ... \n", "... ... ... ... ... \n", "40055 2 8 2 ... \n", "40056 2 9 2 ... \n", "40057 4 10 2 ... \n", "40058 4 10 2 ... \n", "40059 4 10 2 ... \n", "\n", " deposit_type agent company days_in_waiting_list customer_type \\\n", "0 No Deposit NaN NaN 0 Transient \n", "1 No Deposit NaN NaN 0 Transient \n", "2 No Deposit NaN NaN 0 Transient \n", "3 No Deposit 304.0 NaN 0 Transient \n", "4 No Deposit 240.0 NaN 0 Transient \n", "... ... ... ... ... ... \n", "40055 No Deposit 143.0 NaN 0 Transient \n", "40056 No Deposit 250.0 NaN 0 Transient-Party \n", "40057 No Deposit 250.0 NaN 0 Transient \n", "40058 No Deposit 40.0 NaN 0 Contract \n", "40059 No Deposit 69.0 NaN 0 Transient \n", "\n", " adr required_car_parking_spaces total_of_special_requests \\\n", "0 0.00 0 0 \n", "1 0.00 0 0 \n", "2 75.00 0 0 \n", "3 75.00 0 0 \n", "4 98.00 0 1 \n", "... ... ... ... \n", "40055 89.75 0 0 \n", "40056 202.27 0 1 \n", "40057 153.57 0 3 \n", "40058 112.80 0 1 \n", "40059 99.06 0 0 \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", "40055 Check-Out 2017-09-10 \n", "40056 Check-Out 2017-09-10 \n", "40057 Check-Out 2017-09-12 \n", "40058 Check-Out 2017-09-14 \n", "40059 Check-Out 2017-09-14 \n", "\n", "[40060 rows x 32 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mask = DF_raw_hotels['hotel']== 'Resort Hotel'\n", "display(DF_raw_hotels[mask])" ] }, { "cell_type": "markdown", "id": "5df6813f-2633-4c52-8861-60bed3de07b5", "metadata": {}, "source": [ "**Problem 1** Can you create a data frame that:\n", "\n", "1. Selects the columns \"hotel\", \"arrival_date_year\", and \"arrival_date_month\"\n", "2. Sorts by arrival_date_year ascending.\n", "3. Masks to only show the month of \"August\"" ] }, { "cell_type": "code", "execution_count": 9, "id": "1180f2af-dc30-42b6-ac92-935ea07898f0", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.874125Z", "iopub.status.busy": "2026-09-09T23:01:11.873963Z", "iopub.status.idle": "2026-09-09T23:01:11.876018Z", "shell.execute_reply": "2026-09-09T23:01:11.875641Z" } }, "outputs": [], "source": [ "# Your code here\n" ] }, { "cell_type": "markdown", "id": "0b8c7a29-c6d0-455d-914d-5a7196ff7216", "metadata": {}, "source": [ "#### Basic Operators\n", "\n", "| Operator | Definition |\n", "|----------|------------|\n", "|<|less than|\n", "|>|greater than|\n", "|<=| less than or equal to|\n", "|>=| greater than or equal to|\n", "|==| exactly equal to|\n", "|!=| not equal to|\n", "\n", "#### Advanced Operators\n", "\n", "| Operator | Definition |\n", "|----------|------------|\n", "| and | check if two things are both true|\n", "| or | check if one of two things is true|\n", "| in | checks if something is in another thing|\n", "| ! | not checks if something is false|\n", "\n", "**Q4** Try to say whether each cell will print true or false before running the code:" ] }, { "cell_type": "code", "execution_count": 10, "id": "f7bde4cb-1929-47b2-ab80-08730b61e4a9", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.877405Z", "iopub.status.busy": "2026-09-09T23:01:11.877254Z", "iopub.status.idle": "2026-09-09T23:01:11.880191Z", "shell.execute_reply": "2026-09-09T23:01:11.879801Z" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "3<10" ] }, { "cell_type": "code", "execution_count": 11, "id": "be806e74-60c5-44cd-b282-2bc86511c49e", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.881599Z", "iopub.status.busy": "2026-09-09T23:01:11.881446Z", "iopub.status.idle": "2026-09-09T23:01:11.884251Z", "shell.execute_reply": "2026-09-09T23:01:11.883887Z" } }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "10<3" ] }, { "cell_type": "code", "execution_count": 12, "id": "3291fc27-83cd-4fcd-952a-f73b849707f0", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.885633Z", "iopub.status.busy": "2026-09-09T23:01:11.885481Z", "iopub.status.idle": "2026-09-09T23:01:11.888367Z", "shell.execute_reply": "2026-09-09T23:01:11.887996Z" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "3<10 and 2<10" ] }, { "cell_type": "code", "execution_count": 13, "id": "2b52aca6-95da-4ad3-bc4b-e9b8c3c11f4e", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.889712Z", "iopub.status.busy": "2026-09-09T23:01:11.889553Z", "iopub.status.idle": "2026-09-09T23:01:11.892431Z", "shell.execute_reply": "2026-09-09T23:01:11.892053Z" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'cat' == 'cat'" ] }, { "cell_type": "code", "execution_count": 14, "id": "af63a581-704d-4685-927d-eaa8538158f8", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.893848Z", "iopub.status.busy": "2026-09-09T23:01:11.893694Z", "iopub.status.idle": "2026-09-09T23:01:11.896563Z", "shell.execute_reply": "2026-09-09T23:01:11.896184Z" } }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'CAT'=='cat'" ] }, { "cell_type": "code", "execution_count": 15, "id": "d6683f6d-a6f4-4e50-bde1-88738de8556f", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.897940Z", "iopub.status.busy": "2026-09-09T23:01:11.897791Z", "iopub.status.idle": "2026-09-09T23:01:11.900634Z", "shell.execute_reply": "2026-09-09T23:01:11.900268Z" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'CAT'!='cat'" ] }, { "cell_type": "code", "execution_count": 16, "id": "f393facf-8051-4c78-a203-1d56c82525b8", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.902003Z", "iopub.status.busy": "2026-09-09T23:01:11.901853Z", "iopub.status.idle": "2026-09-09T23:01:11.904687Z", "shell.execute_reply": "2026-09-09T23:01:11.904304Z" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2<=2" ] }, { "cell_type": "code", "execution_count": 17, "id": "0a7a484d-6a34-40ee-b1af-ad87a941bd76", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.906006Z", "iopub.status.busy": "2026-09-09T23:01:11.905858Z", "iopub.status.idle": "2026-09-09T23:01:11.908718Z", "shell.execute_reply": "2026-09-09T23:01:11.908341Z" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2<=2 or 2<1" ] }, { "cell_type": "code", "execution_count": 18, "id": "d53501f7-6c98-4469-a972-58fe3730c253", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.910047Z", "iopub.status.busy": "2026-09-09T23:01:11.909897Z", "iopub.status.idle": "2026-09-09T23:01:11.912853Z", "shell.execute_reply": "2026-09-09T23:01:11.912478Z" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_numbers = [3,6,18,42]\n", "42 in my_numbers" ] }, { "cell_type": "markdown", "id": "51cdf704-b615-4dde-828f-228205e74d4a", "metadata": {}, "source": [ "**Q5** Create 5 more logical operator tests and show the results." ] }, { "cell_type": "code", "execution_count": 19, "id": "6a4cde01-1642-475f-a182-94c2396ee3dd", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.914273Z", "iopub.status.busy": "2026-09-09T23:01:11.914123Z", "iopub.status.idle": "2026-09-09T23:01:11.916098Z", "shell.execute_reply": "2026-09-09T23:01:11.915724Z" } }, "outputs": [], "source": [ "# Your code here\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "c8e2ef3d-5a0f-4932-9581-7b18e9cd594e", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.917415Z", "iopub.status.busy": "2026-09-09T23:01:11.917261Z", "iopub.status.idle": "2026-09-09T23:01:11.919292Z", "shell.execute_reply": "2026-09-09T23:01:11.918909Z" } }, "outputs": [], "source": [ "# Your code here\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "3ab96a18-1e73-4a50-b7f0-446eae8f0a75", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.920702Z", "iopub.status.busy": "2026-09-09T23:01:11.920541Z", "iopub.status.idle": "2026-09-09T23:01:11.922545Z", "shell.execute_reply": "2026-09-09T23:01:11.922165Z" } }, "outputs": [], "source": [ "# Your code here\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "b0354c53-3847-415a-8734-8880f5387c3d", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.923899Z", "iopub.status.busy": "2026-09-09T23:01:11.923747Z", "iopub.status.idle": "2026-09-09T23:01:11.925733Z", "shell.execute_reply": "2026-09-09T23:01:11.925349Z" } }, "outputs": [], "source": [ "# Your code here\n" ] }, { "cell_type": "code", "execution_count": 23, "id": "12cf2b75-6da1-4866-8853-164dee7cc9f8", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.927077Z", "iopub.status.busy": "2026-09-09T23:01:11.926925Z", "iopub.status.idle": "2026-09-09T23:01:11.928937Z", "shell.execute_reply": "2026-09-09T23:01:11.928543Z" } }, "outputs": [], "source": [ "# Your code here\n" ] }, { "cell_type": "markdown", "id": "1be81adb-6c24-41d7-84c3-750941aca771", "metadata": {}, "source": [ "### Follow Along - Example Analysis\n", "\n", "Now that we know some logical operators we can create fancier boolean masks and get more interesting subsets of our data. Here is a mini analysis of our data using what we know so far. See if you can figure out what each command is doing. \n", "\n", "**Q6** Add your comments explaining the code:\n" ] }, { "cell_type": "code", "execution_count": 24, "id": "8868552c-35e7-4534-b7ba-99d3882ce97e", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.930372Z", "iopub.status.busy": "2026-09-09T23:01:11.930218Z", "iopub.status.idle": "2026-09-09T23:01:11.939560Z", "shell.execute_reply": "2026-09-09T23:01:11.939173Z" } }, "outputs": [ { "data": { "text/plain": [ "country\n", "PRT 48590\n", "GBR 12129\n", "FRA 10415\n", "ESP 8568\n", "DEU 7287\n", " ... \n", "MRT 1\n", "KIR 1\n", "SDN 1\n", "ATF 1\n", "SLE 1\n", "Name: count, Length: 177, dtype: int64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Add your comments: \n", "DF_raw_hotels['country'].value_counts()" ] }, { "cell_type": "markdown", "id": "3200de72-7acc-4ecd-977f-31ae0031fe43", "metadata": {}, "source": [ "Wow there is a lot of data for PRT (Portugal)!" ] }, { "cell_type": "code", "execution_count": 25, "id": "b4da6a98-7728-4f4b-ba9e-ad0f96dddc20", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.941024Z", "iopub.status.busy": "2026-09-09T23:01:11.940869Z", "iopub.status.idle": "2026-09-09T23:01:11.957341Z", "shell.execute_reply": "2026-09-09T23:01:11.956828Z" } }, "outputs": [ { "data": { "text/html": [ "
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hotelarrival_date_yearis_canceledarrival_date_monthcountry
17Resort Hotel20150JulyIRL
16Resort Hotel20150JulyPRT
15Resort Hotel20150JulyIRL
14Resort Hotel20150JulyPRT
23167Resort Hotel20150OctoberGBR
..................
119354City Hotel20170AugustSWE
22041Resort Hotel20170JanuaryPRT
22042Resort Hotel20170JanuaryPRT
22043Resort Hotel20170AprilPRT
119389City Hotel20170AugustDEU
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119390 rows × 5 columns

\n", "
" ], "text/plain": [ " hotel arrival_date_year is_canceled arrival_date_month \\\n", "17 Resort Hotel 2015 0 July \n", "16 Resort Hotel 2015 0 July \n", "15 Resort Hotel 2015 0 July \n", "14 Resort Hotel 2015 0 July \n", "23167 Resort Hotel 2015 0 October \n", "... ... ... ... ... \n", "119354 City Hotel 2017 0 August \n", "22041 Resort Hotel 2017 0 January \n", "22042 Resort Hotel 2017 0 January \n", "22043 Resort Hotel 2017 0 April \n", "119389 City Hotel 2017 0 August \n", "\n", " country \n", "17 IRL \n", "16 PRT \n", "15 IRL \n", "14 PRT \n", "23167 GBR \n", "... ... \n", "119354 SWE \n", "22041 PRT \n", "22042 PRT \n", "22043 PRT \n", "119389 DEU \n", "\n", "[119390 rows x 5 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Here I am defining the variables (columns) I care about\n", "my_country = 'PRT'\n", "my_columns = ['hotel',\n", " 'arrival_date_year',\n", " 'is_canceled', \n", " 'arrival_date_month',\n", " 'country']\n", "sort_column = 'arrival_date_year'\n", "\n", "# Add your comments:\n", "DF_mini_analysis = DF_raw_hotels[my_columns].sort_values(sort_column).copy()\n", "display(DF_mini_analysis)" ] }, { "cell_type": "code", "execution_count": 26, "id": "8a9cf9b0-d1c8-4a9d-8e10-6a57b8d542b7", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.958785Z", "iopub.status.busy": "2026-09-09T23:01:11.958617Z", "iopub.status.idle": "2026-09-09T23:01:11.973137Z", "shell.execute_reply": "2026-09-09T23:01:11.972673Z" } }, "outputs": [ { "data": { "text/html": [ "
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hotelarrival_date_yearis_canceledarrival_date_monthcountry
16Resort Hotel20150JulyPRT
14Resort Hotel20150JulyPRT
40061City Hotel20151JulyPRT
40060City Hotel20150JulyPRT
40077City Hotel20150JulyPRT
..................
119317City Hotel20170AugustPRT
119357City Hotel20170AugustPRT
22041Resort Hotel20170JanuaryPRT
22042Resort Hotel20170JanuaryPRT
22043Resort Hotel20170AprilPRT
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48590 rows × 5 columns

\n", "
" ], "text/plain": [ " hotel arrival_date_year is_canceled arrival_date_month \\\n", "16 Resort Hotel 2015 0 July \n", "14 Resort Hotel 2015 0 July \n", "40061 City Hotel 2015 1 July \n", "40060 City Hotel 2015 0 July \n", "40077 City Hotel 2015 0 July \n", "... ... ... ... ... \n", "119317 City Hotel 2017 0 August \n", "119357 City Hotel 2017 0 August \n", "22041 Resort Hotel 2017 0 January \n", "22042 Resort Hotel 2017 0 January \n", "22043 Resort Hotel 2017 0 April \n", "\n", " country \n", "16 PRT \n", "14 PRT \n", "40061 PRT \n", "40060 PRT \n", "40077 PRT \n", "... ... \n", "119317 PRT \n", "119357 PRT \n", "22041 PRT \n", "22042 PRT \n", "22043 PRT \n", "\n", "[48590 rows x 5 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# What does this first mask do?\n", "# Your comments:\n", "mask_country = DF_mini_analysis['country']==my_country\n", "DF_mini_analysis_masked = DF_mini_analysis[mask_country]\n", "display(DF_mini_analysis_masked)" ] }, { "cell_type": "code", "execution_count": 27, "id": "b52575a1-4dc8-40ef-89cb-1102bf0a152f", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.974615Z", "iopub.status.busy": "2026-09-09T23:01:11.974459Z", "iopub.status.idle": "2026-09-09T23:01:11.982404Z", "shell.execute_reply": "2026-09-09T23:01:11.982021Z" } }, "outputs": [ { "data": { "text/html": [ "
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hotelarrival_date_yearis_canceledarrival_date_monthcountry
78694City Hotel20160JanuaryPRT
78695City Hotel20160JanuaryPRT
78696City Hotel20160JanuaryPRT
78697City Hotel20160FebruaryPRT
78698City Hotel20160FebruaryPRT
..................
119317City Hotel20170AugustPRT
119357City Hotel20170AugustPRT
22041Resort Hotel20170JanuaryPRT
22042Resort Hotel20170JanuaryPRT
22043Resort Hotel20170AprilPRT
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35283 rows × 5 columns

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" ], "text/plain": [ " hotel arrival_date_year is_canceled arrival_date_month \\\n", "78694 City Hotel 2016 0 January \n", "78695 City Hotel 2016 0 January \n", "78696 City Hotel 2016 0 January \n", "78697 City Hotel 2016 0 February \n", "78698 City Hotel 2016 0 February \n", "... ... ... ... ... \n", "119317 City Hotel 2017 0 August \n", "119357 City Hotel 2017 0 August \n", "22041 Resort Hotel 2017 0 January \n", "22042 Resort Hotel 2017 0 January \n", "22043 Resort Hotel 2017 0 April \n", "\n", " country \n", "78694 PRT \n", "78695 PRT \n", "78696 PRT \n", "78697 PRT \n", "78698 PRT \n", "... ... \n", "119317 PRT \n", "119357 PRT \n", "22041 PRT \n", "22042 PRT \n", "22043 PRT \n", "\n", "[35283 rows x 5 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# What does this second mask do?\n", "# Your comments:\n", "mask_years = DF_mini_analysis_masked['arrival_date_year']>2015\n", "DF_mini_analysis_masked = DF_mini_analysis_masked[mask_years] \n", "# Notice that I wrote over my data in the command above\n", "\n", "display(DF_mini_analysis_masked)" ] }, { "cell_type": "code", "execution_count": 28, "id": "13fe9b2a-5a48-4944-8b77-fa8bc3e4fc0e", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:11.983763Z", "iopub.status.busy": "2026-09-09T23:01:11.983604Z", "iopub.status.idle": "2026-09-09T23:01:12.175065Z", "shell.execute_reply": "2026-09-09T23:01:12.174579Z" } }, "outputs": [ { "data": { "text/html": [ " \n", " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.histogram(DF_mini_analysis_masked,\n", " x='arrival_date_month',\n", " color='hotel',\n", " facet_col='is_canceled',\n", " facet_col_wrap=1)\n", "\n", "fig.update_layout(title='Number of Hotels Booked Each Month in Portugal',\n", " title_x=0.5,\n", " xaxis_title=\"Month\",\n", " autosize=False,\n", " width=800,\n", " height=500)\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "b5260515-0d9d-493f-bda5-93ac14ed697b", "metadata": {}, "source": [ "**Q7** What exactly is this histogram showing and what is excluded based on the analysis we did above? Is there anything interesting about the graph? Any questions raised?" ] }, { "cell_type": "markdown", "id": "9d3e7e43-fcfd-4b94-b3b4-9307cc4f21de", "metadata": {}, "source": [ "**(Double Click Here)**" ] }, { "cell_type": "markdown", "id": "b8f37f81-5348-4d73-9a6e-2cc61b8dbf41", "metadata": {}, "source": [ "**Problem 2** Now it's your turn to do a mini analysis. Please do the following:\n", "\n", "1. Choose a subset of columns that you are interested in and look just at those column.\n", "2. Make at least one mask that will remove some of the data (eg. look only at one categorical value OR one or more numerical values.\n", "3. Sort the data using one of the columns.\n", "4. Make a histogram or bar graph of your final data set.\n", "5. Describe in words what you see in your graph, or if there is anything interesting there." ] }, { "cell_type": "code", "execution_count": 29, "id": "6f599fda-c047-4115-bb11-2db27d0dd724", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:12.178183Z", "iopub.status.busy": "2026-09-09T23:01:12.178021Z", "iopub.status.idle": "2026-09-09T23:01:12.180200Z", "shell.execute_reply": "2026-09-09T23:01:12.179785Z" } }, "outputs": [], "source": [ "# Your code here - you will probably need more than one cell\n" ] }, { "cell_type": "markdown", "id": "a17327fb-7c11-4dbe-b565-05ccb5d59f4a", "metadata": {}, "source": [ "# End Of Homework\n", "\n", "### Everything below here is extra - just so you have a copy of the code I showed in lecture.\n", "\n", "### Excluding variables - removing one column (Extra)\n", "\n", "There are a few ways to get rid of columns. The first would be to write down ALL the columns and leave out one. Let's say we want to leave out information about the \"agent\" variable." ] }, { "cell_type": "code", "execution_count": 30, "id": "568b8ced-fad9-450c-b488-703aef19d734", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:12.181636Z", "iopub.status.busy": "2026-09-09T23:01:12.181489Z", "iopub.status.idle": "2026-09-09T23:01:12.237399Z", "shell.execute_reply": "2026-09-09T23:01:12.236951Z" } }, "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...booking_changesdeposit_typecompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
0Resort Hotel03422015July271002...3No DepositNaN0Transient0.0000Check-Out2015-07-01
1Resort Hotel07372015July271002...4No DepositNaN0Transient0.0000Check-Out2015-07-01
2Resort Hotel072015July271011...0No DepositNaN0Transient75.0000Check-Out2015-07-02
3Resort Hotel0132015July271011...0No DepositNaN0Transient75.0000Check-Out2015-07-02
4Resort Hotel0142015July271022...0No DepositNaN0Transient98.0001Check-Out2015-07-03
..................................................................
119385City Hotel0232017August3530252...0No DepositNaN0Transient96.1400Check-Out2017-09-06
119386City Hotel01022017August3531253...0No DepositNaN0Transient225.4302Check-Out2017-09-07
119387City Hotel0342017August3531252...0No DepositNaN0Transient157.7104Check-Out2017-09-07
119388City Hotel01092017August3531252...0No DepositNaN0Transient104.4000Check-Out2017-09-07
119389City Hotel02052017August3529272...0No DepositNaN0Transient151.2002Check-Out2017-09-07
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119390 rows × 31 columns

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" ], "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 ... booking_changes deposit_type \\\n", "0 0 2 ... 3 No Deposit \n", "1 0 2 ... 4 No Deposit \n", "2 1 1 ... 0 No Deposit \n", "3 1 1 ... 0 No Deposit \n", "4 2 2 ... 0 No Deposit \n", "... ... ... ... ... ... \n", "119385 5 2 ... 0 No Deposit \n", "119386 5 3 ... 0 No Deposit \n", "119387 5 2 ... 0 No Deposit \n", "119388 5 2 ... 0 No Deposit \n", "119389 7 2 ... 0 No Deposit \n", "\n", " company days_in_waiting_list customer_type adr \\\n", "0 NaN 0 Transient 0.00 \n", "1 NaN 0 Transient 0.00 \n", "2 NaN 0 Transient 75.00 \n", "3 NaN 0 Transient 75.00 \n", "4 NaN 0 Transient 98.00 \n", "... ... ... ... ... \n", "119385 NaN 0 Transient 96.14 \n", "119386 NaN 0 Transient 225.43 \n", "119387 NaN 0 Transient 157.71 \n", "119388 NaN 0 Transient 104.40 \n", "119389 NaN 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 31 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# First get all of them\n", "my_columns = list(DF_raw_hotels.keys())\n", "# Then remove the one you don't want\n", "my_columns.remove('agent')\n", "# Then get the new data frame\n", "DF_raw_hotels[my_columns]" ] }, { "cell_type": "markdown", "id": "193511d0-f894-4d1c-bf42-226e02751e9c", "metadata": {}, "source": [ "### Fancy ways to get columns (Extra - ADVANCED)\n", "\n", "There are fancy ways to get a list of columns that you might be interested in.\n", "\n", "#### Starts with\n", "\n", "You can use the command **startswith()** to check if a variable starts with a word or part of a word. Here is code to get only columns that start with the word \"arrival\":" ] }, { "cell_type": "code", "execution_count": 31, "id": "5f544961-5061-486e-8056-22ac21064895", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:12.238983Z", "iopub.status.busy": "2026-09-09T23:01:12.238824Z", "iopub.status.idle": "2026-09-09T23:01:12.241984Z", "shell.execute_reply": "2026-09-09T23:01:12.241552Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['arrival_date_year', 'arrival_date_month', 'arrival_date_week_number', 'arrival_date_day_of_month']\n" ] } ], "source": [ "# Get the list of all the columns\n", "columns_list = list(DF_raw_hotels.keys())\n", "# Get true false values using the startswith() comand\n", "column_mask = [column.startswith('arrival') for column in columns_list]\n", "# Mask the column names to get just the ones you want and make a list\n", "my_columns = list(DF_raw_hotels.keys()[column_mask])\n", "print(my_columns)" ] }, { "cell_type": "markdown", "id": "28f643ca-c554-4884-ab7f-462781491ba5", "metadata": {}, "source": [ "#### Ends with\n", "\n", "You can use the command **endswith()** to check if a variable ends with a word or part of a word. Here is code to get only columns that end with the word \"type\":" ] }, { "cell_type": "code", "execution_count": 32, "id": "cf9d2596-63f1-49a9-9abe-daa4c26330a2", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:12.243628Z", "iopub.status.busy": "2026-09-09T23:01:12.243451Z", "iopub.status.idle": "2026-09-09T23:01:12.246280Z", "shell.execute_reply": "2026-09-09T23:01:12.245877Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['reserved_room_type', 'assigned_room_type', 'deposit_type', 'customer_type']\n" ] } ], "source": [ "# Get the list of all the columns\n", "columns_list = list(DF_raw_hotels.keys())\n", "# Get true false values using the startswith() comand\n", "column_mask = [column.endswith('type') for column in columns_list]\n", "# Mask the column names to get just the ones you want and make a list\n", "my_columns = list(DF_raw_hotels.keys()[column_mask])\n", "print(my_columns)" ] }, { "cell_type": "markdown", "id": "996a955f-94b0-4cce-bb53-91c596bb8ff4", "metadata": {}, "source": [ "#### IN\n", "\n", "You can use the Python check **is in** to check if a variable contains a word or part of a word. Here is code to get only columns that contain the word \"date\":" ] }, { "cell_type": "code", "execution_count": 33, "id": "b9d27002-f922-4315-b0c9-d45dca31ad8d", "metadata": { "execution": { "iopub.execute_input": "2026-09-09T23:01:12.247806Z", "iopub.status.busy": "2026-09-09T23:01:12.247641Z", "iopub.status.idle": "2026-09-09T23:01:12.250640Z", "shell.execute_reply": "2026-09-09T23:01:12.250241Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['arrival_date_year', 'arrival_date_month', 'arrival_date_week_number', 'arrival_date_day_of_month', 'reservation_status_date']\n" ] } ], "source": [ "# Get the list of all the columns\n", "columns_list = list(DF_raw_hotels.keys())\n", "# Get true false values using the startswith() comand\n", "column_mask = [\"date\" in column for column in columns_list]\n", "# Mask the column names to get just the ones you want and make a list\n", "my_columns = list(DF_raw_hotels.keys()[column_mask])\n", "print(my_columns)" ] }, { "cell_type": "code", "execution_count": null, "id": "46f71d59-92bc-4c6a-a722-a32b44574bb1", "metadata": {}, "outputs": [], "source": [] } ], "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 }