{ "cells": [ { "cell_type": "markdown", "id": "ee2c56a0", "metadata": {}, "source": [ "# Adara Challenge Analysis" ] }, { "cell_type": "markdown", "id": "3bdd5710", "metadata": {}, "source": [ "## 1. Exploratory Data Analysis" ] }, { "cell_type": "markdown", "id": "8c9927d5", "metadata": {}, "source": [ "### 1.1 Data Dictionary\n", "\n", "The data contains events from the months of January 2019 and January 2021; the 2019 can be used as a basis of comparison for the activity in 2021.\n", "\n", "The destinations contained in the data are selected top-tier cities in Germany, UK, France, Italy, and Spain\n", "\n", "The table below shows the definition of attributes:" ] }, { "cell_type": "markdown", "id": "7b2ec174", "metadata": {}, "source": [ "Attributes |Definition\n", ":-----|:---------------\n", "**event_date** |The date of the search or booking activity. This is different from the departure_checkin_date, which is the actual date of travel activity: if a customer makes a booking today for check-in on 1 October, today’s date would be the event date, and 1 October would be the “departure_checkin_date”\n", "**activity_group** |An indicator of the type of event on the hotel website. Classified as “Searching” (a general search for a room) or “Booking” (an actual reservation)\n", "**reason_for_travel_detailed** |this is the Reason for Travel, categorized as either “Business”, “Leisure-Non-Family” (leisure travel consisting of 1 or 2 people), and “Leisure-Family” (leisure travel consisting of 3 or more people)\n", "**origin_country_code** |2-letter ISO country code indicating the origin country from which the booking or search activity was made\n", "**hotel_city,hotel_state,hotel_country** |raw information specifying the destination in question.
Note that this data is in an _unclean_ state, provided straight from the source system.\n", "**traveler_value_group** |Indicator of traveler value (overall spend + loyalty status), grouped into “Low”, “Medium”, “High”\n", "**departure_checkin_date** |Start date of hotel stay; see _event_date_ to determine how the date information differs.\n", "**total_number_events** |Aggregated count of total number of events" ] }, { "cell_type": "markdown", "id": "6c18b366", "metadata": {}, "source": [ "### 1.2 Data Exploration\n", "\n", "#### **Import Python packages and raw data**" ] }, { "cell_type": "code", "execution_count": 1, "id": "576927bc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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event_dateactivity_groupreason_for_travel_detailedorigin_country_codehotel_cityhotel_statehotel_countrytraveler_value_groupdeparture_checkin_datetotal_number_events
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" ], "text/plain": [ " event_date activity_group reason_for_travel_detailed origin_country_code \\\n", "0 2019-01-07 Booking Leisure - Non-Family DE \n", "1 2019-01-16 Searching Business GB \n", "2 2021-01-28 Searching Leisure - Non-Family DE \n", "3 2021-01-28 Searching Leisure - Non-Family US \n", "4 2019-01-03 Searching Business DE \n", "\n", " hotel_city hotel_state hotel_country traveler_value_group \\\n", "0 dusseldorf NaN de Med \n", "1 dusseldorf NaN de Med \n", "2 dusseldorf NaN de Low \n", "3 dusseldorf NaN de Low \n", "4 dusseldorf NaN de Low \n", "\n", " departure_checkin_date total_number_events \n", "0 2019-01-25 1 \n", "1 2019-02-05 1 \n", "2 2021-01-29 1 \n", "3 2021-07-04 2 \n", "4 2019-01-31 2 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#data manipulation\n", "import pandas as pd \n", "\n", "#data visualization\n", "import plotly.express as px \n", "import plotly.graph_objects as go\n", "import plotly.io as pio\n", "pio.templates.default = \"plotly_white\"\n", "\n", "# interactive data table\n", "import qgrid as dt \n", "\n", "#read raw data as panda dataframe\n", "raw_data = pd.read_csv(\"./sample_data.csv\")\n", "\n", "#Glimpse of imported raw csv data\n", "raw_data.head()\n" ] }, { "cell_type": "markdown", "id": "26fcdf28", "metadata": {}, "source": [ "#### **Summary Statistics**" ] }, { "cell_type": "code", "execution_count": 2, "id": "d88e8f7d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 724795 entries, 0 to 724794\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 event_date 724795 non-null object\n", " 1 activity_group 724795 non-null object\n", " 2 reason_for_travel_detailed 724795 non-null object\n", " 3 origin_country_code 723546 non-null object\n", " 4 hotel_city 724795 non-null object\n", " 5 hotel_state 55580 non-null object\n", " 6 hotel_country 714652 non-null object\n", " 7 traveler_value_group 724795 non-null object\n", " 8 departure_checkin_date 724795 non-null object\n", " 9 total_number_events 724795 non-null int64 \n", "dtypes: int64(1), object(9)\n", "memory usage: 55.3+ MB\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " total_number_events\n", "count 724795.000000\n", "mean 2.295108\n", "std 4.650648\n", "min 1.000000\n", "25% 1.000000\n", "50% 1.000000\n", "75% 2.000000\n", "max 390.000000" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data.info()\n", "raw_data.describe()" ] }, { "cell_type": "markdown", "id": "2080625f", "metadata": {}, "source": [ "#### **Data Wrangling**" ] }, { "cell_type": "markdown", "id": "b3dca0d8", "metadata": {}, "source": [ "- *Transformed event_date and departure_checkin_date from object to date*\n", "- *Added event_wday, event_year, event_mday, checkin_wday, checkin_year, checkin_month attribues for a better understanding of total_number_events according to dates.*" ] }, { "cell_type": "code", "execution_count": 3, "id": "554d0bb1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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02019-01-07BookingLeisure - Non-FamilyDEdusseldorfNaNdeMed2019-01-251Mon2019Jan 07Fri2019Jan
12019-01-16SearchingBusinessGBdusseldorfNaNdeMed2019-02-051Wed2019Jan 16Tue2019Feb
22021-01-28SearchingLeisure - Non-FamilyDEdusseldorfNaNdeLow2021-01-291Thu2021Jan 28Fri2021Jan
32021-01-28SearchingLeisure - Non-FamilyUSdusseldorfNaNdeLow2021-07-042Thu2021Jan 28Sun2021Jul
42019-01-03SearchingBusinessDEdusseldorfNaNdeLow2019-01-312Thu2019Jan 03Thu2019Jan
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" ], "text/plain": [ " event_date activity_group reason_for_travel_detailed origin_country_code \\\n", "0 2019-01-07 Booking Leisure - Non-Family DE \n", "1 2019-01-16 Searching Business GB \n", "2 2021-01-28 Searching Leisure - Non-Family DE \n", "3 2021-01-28 Searching Leisure - Non-Family US \n", "4 2019-01-03 Searching Business DE \n", "\n", " hotel_city hotel_state hotel_country traveler_value_group \\\n", "0 dusseldorf NaN de Med \n", "1 dusseldorf NaN de Med \n", "2 dusseldorf NaN de Low \n", "3 dusseldorf NaN de Low \n", "4 dusseldorf NaN de Low \n", "\n", " departure_checkin_date total_number_events event_wday event_year \\\n", "0 2019-01-25 1 Mon 2019 \n", "1 2019-02-05 1 Wed 2019 \n", "2 2021-01-29 1 Thu 2021 \n", "3 2021-07-04 2 Thu 2021 \n", "4 2019-01-31 2 Thu 2019 \n", "\n", " event_mday checkin_wday checkin_year checkin_month \n", "0 Jan 07 Fri 2019 Jan \n", "1 Jan 16 Tue 2019 Feb \n", "2 Jan 28 Fri 2021 Jan \n", "3 Jan 28 Sun 2021 Jul \n", "4 Jan 03 Thu 2019 Jan " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[\"event_date\"] = pd.to_datetime(raw_data[\"event_date\"])\n", "raw_data[\"event_wday\"] = raw_data[\"event_date\"].dt.day_name().str[:3]\n", "raw_data[\"event_year\"] = raw_data[\"event_date\"].dt.year\n", "raw_data[\"event_mday\"] = raw_data[\"event_date\"].dt.month_name().str[:3].str.cat(raw_data[\"event_date\"].dt.day.astype(str).str.zfill(2), sep =\" \")\n", "\n", "raw_data[\"departure_checkin_date\"] = pd.to_datetime(raw_data[\"departure_checkin_date\"])\n", "raw_data[\"checkin_wday\"] = raw_data[\"departure_checkin_date\"].dt.day_name().str[:3]\n", "raw_data[\"checkin_year\"] = raw_data[\"departure_checkin_date\"].dt.year\n", "raw_data[\"checkin_month\"] = raw_data[\"departure_checkin_date\"].dt.month_name().str[:3]\n", "\n", "#check new columns\n", "raw_data.head()" ] }, { "cell_type": "markdown", "id": "b0822a8f", "metadata": {}, "source": [ "- *Convert datatype from object to categorical to save memory*" ] }, { "cell_type": "code", "execution_count": 4, "id": "fbcdb85e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 724795 entries, 0 to 724794\n", "Data columns (total 16 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 event_date 724795 non-null datetime64[ns]\n", " 1 activity_group 724795 non-null object \n", " 2 reason_for_travel_detailed 724795 non-null object \n", " 3 origin_country_code 723546 non-null object \n", " 4 hotel_city 724795 non-null object \n", " 5 hotel_state 55580 non-null object \n", " 6 hotel_country 714652 non-null object \n", " 7 traveler_value_group 724795 non-null object \n", " 8 departure_checkin_date 724795 non-null datetime64[ns]\n", " 9 total_number_events 724795 non-null int64 \n", " 10 event_wday 724795 non-null object \n", " 11 event_year 724795 non-null int64 \n", " 12 event_mday 724795 non-null object \n", " 13 checkin_wday 724795 non-null object \n", " 14 checkin_year 724795 non-null int64 \n", " 15 checkin_month 724795 non-null object \n", "dtypes: datetime64[ns](2), int64(3), object(11)\n", "memory usage: 88.5+ MB\n" ] } ], "source": [ "raw_data.info()" ] }, { "cell_type": "code", "execution_count": 5, "id": "b88e2762", "metadata": {}, "outputs": [], "source": [ "raw_data[\"activity_group\"] = raw_data[\"activity_group\"].astype(\"category\")\n", "raw_data[\"reason_for_travel_detailed\"] = raw_data[\"reason_for_travel_detailed\"].astype(\"category\")\n", "raw_data[\"origin_country_code\"] = raw_data[\"origin_country_code\"].astype(\"category\")\n", "raw_data[\"hotel_city\"] = raw_data[\"hotel_city\"].astype(\"category\")\n", "raw_data[\"hotel_state\"] = raw_data[\"hotel_state\"].astype(\"category\")\n", "raw_data[\"hotel_country\"] = raw_data[\"hotel_country\"].astype(\"category\")\n", "raw_data[\"event_year\"] = raw_data[\"event_year\"].astype(\"category\")\n", "raw_data[\"event_mday\"] = raw_data[\"event_mday\"].astype(\"category\")\n", "raw_data[\"checkin_year\"] = raw_data[\"checkin_year\"].astype(\"category\")" ] }, { "cell_type": "markdown", "id": "b857b1c4", "metadata": {}, "source": [ "- *Transformed traveler_value_group into ordinal based on ascending order of Low, Med, and High.*" ] }, { "cell_type": "code", "execution_count": 6, "id": "d85171dc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Low 382542\n", "Med 245172\n", "High 96496\n", "None 585\n", "Name: traveler_value_group, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[\"traveler_value_group\"] = raw_data[\"traveler_value_group\"].astype(\"category\")\n", "raw_data[\"traveler_value_group\"].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 7, "id": "f2efe994", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Low 382542\n", "Med 245172\n", "High 96496\n", "NaN 585\n", "Name: traveler_value_group, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[\"traveler_value_group\"] = raw_data[\"traveler_value_group\"].cat.set_categories(\n", " new_categories=[\"Low\", \"Med\", \"High\"],\n", " ordered=True\n", ")\n", "#check if the values are the same\n", "raw_data[\"traveler_value_group\"].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 8, "id": "37302f43", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Med\n", "1 Med\n", "2 Low\n", "3 Low\n", "4 Low\n", "Name: traveler_value_group, dtype: category\n", "Categories (3, object): ['Low' < 'Med' < 'High']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#check order\n", "raw_data[\"traveler_value_group\"].head()" ] }, { "cell_type": "markdown", "id": "db0d7242", "metadata": {}, "source": [ "- *Transformed event_wday, checkin_wday, and checkin_month into ordinal based on calendar weekday and month*" ] }, { "cell_type": "code", "execution_count": 9, "id": "6e6b2db7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Wed 118545\n", "Tue 115030\n", "Thu 114955\n", "Mon 103738\n", "Fri 96362\n", "Sun 93786\n", "Sat 82379\n", "Name: event_wday, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[\"event_wday\"] = raw_data[\"event_wday\"].astype(\"category\")\n", "raw_data[\"event_wday\"].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 10, "id": "b44c54ae", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Wed 118545\n", "Tue 115030\n", "Thu 114955\n", "Mon 103738\n", "Fri 96362\n", "Sun 93786\n", "Sat 82379\n", "Name: event_wday, dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[\"event_wday\"] = raw_data[\"event_wday\"].cat.set_categories(\n", " new_categories=[\"Sun\", \"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\"],\n", " ordered=True\n", ")\n", "#check if the values are the same\n", "raw_data[\"event_wday\"].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 11, "id": "46acb346", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Mon\n", "1 Wed\n", "2 Thu\n", "3 Thu\n", "4 Thu\n", "Name: event_wday, dtype: category\n", "Categories (7, object): ['Sun' < 'Mon' < 'Tue' < 'Wed' < 'Thu' < 'Fri' < 'Sat']" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#check order\n", "raw_data[\"event_wday\"].head()" ] }, { "cell_type": "code", "execution_count": 12, "id": "6797e0a5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Fri 146583\n", "Sat 119643\n", "Thu 113442\n", "Mon 96403\n", "Wed 89219\n", "Tue 80646\n", "Sun 78859\n", "Name: checkin_wday, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[\"checkin_wday\"] = raw_data[\"checkin_wday\"].astype(\"category\")\n", "raw_data[\"checkin_wday\"].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 13, "id": "8f8ae7b9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Fri 146583\n", "Sat 119643\n", "Thu 113442\n", "Mon 96403\n", "Wed 89219\n", "Tue 80646\n", "Sun 78859\n", "Name: checkin_wday, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[\"checkin_wday\"] = raw_data[\"checkin_wday\"].cat.set_categories(\n", " new_categories=[\"Sun\", \"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\"],\n", " ordered=True\n", ")\n", "#check if the values are the same\n", "raw_data[\"checkin_wday\"].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 14, "id": "e19c3e03", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Fri\n", "1 Tue\n", "2 Fri\n", "3 Sun\n", "4 Thu\n", "Name: checkin_wday, dtype: category\n", "Categories (7, object): ['Sun' < 'Mon' < 'Tue' < 'Wed' < 'Thu' < 'Fri' < 'Sat']" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#check order\n", "raw_data[\"checkin_wday\"].head()" ] }, { "cell_type": "code", "execution_count": 15, "id": "ea8fe0aa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Jan 168204\n", "Feb 156079\n", "Mar 96787\n", "Apr 76144\n", "May 59092\n", "Jun 43935\n", "Jul 35551\n", "Aug 29727\n", "Sep 23066\n", "Oct 16083\n", "Dec 11044\n", "Nov 9083\n", "Name: checkin_month, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[\"checkin_month\"] = raw_data[\"checkin_month\"].astype(\"category\")\n", "raw_data[\"checkin_month\"].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 16, "id": "e051bf14", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Jan 168204\n", "Feb 156079\n", "Mar 96787\n", "Apr 76144\n", "May 59092\n", "Jun 43935\n", "Jul 35551\n", "Aug 29727\n", "Sep 23066\n", "Oct 16083\n", "Dec 11044\n", "Nov 9083\n", "Name: checkin_month, dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[\"checkin_month\"] = raw_data[\"checkin_month\"].cat.set_categories(\n", " new_categories=[\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"],\n", " ordered=True\n", ")\n", "#check order\n", "raw_data[\"checkin_month\"].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 17, "id": "61b463b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Jan\n", "1 Feb\n", "2 Jan\n", "3 Jul\n", "4 Jan\n", "Name: checkin_month, dtype: category\n", "Categories (12, object): ['Jan' < 'Feb' < 'Mar' < 'Apr' ... 'Sep' < 'Oct' < 'Nov' < 'Dec']" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#check order\n", "raw_data[\"checkin_month\"].head()" ] }, { "cell_type": "code", "execution_count": 18, "id": "4b5cc589", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 724795 entries, 0 to 724794\n", "Data columns (total 16 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 event_date 724795 non-null datetime64[ns]\n", " 1 activity_group 724795 non-null category \n", " 2 reason_for_travel_detailed 724795 non-null category \n", " 3 origin_country_code 723546 non-null category \n", " 4 hotel_city 724795 non-null category \n", " 5 hotel_state 55580 non-null category \n", " 6 hotel_country 714652 non-null category \n", " 7 traveler_value_group 724210 non-null category \n", " 8 departure_checkin_date 724795 non-null datetime64[ns]\n", " 9 total_number_events 724795 non-null int64 \n", " 10 event_wday 724795 non-null category \n", " 11 event_year 724795 non-null category \n", " 12 event_mday 724795 non-null category \n", " 13 checkin_wday 724795 non-null category \n", " 14 checkin_year 724795 non-null category \n", " 15 checkin_month 724795 non-null category \n", "dtypes: category(13), datetime64[ns](2), int64(1)\n", "memory usage: 27.0 MB\n" ] } ], "source": [ "#final check of datatype and memory savings\n", "raw_data.info()" ] }, { "cell_type": "markdown", "id": "41c4e200", "metadata": {}, "source": [ "#### Event Year EDA" ] }, { "cell_type": "code", "execution_count": 19, "id": "bc77cf70", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "customdata": [ [ 77.3138649448176 ] ], "hovertemplate": "event_year=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "2019", "marker": { "color": "darkgray", "pattern": { "shape": "" } }, "name": "2019", "offsetgroup": "2019", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ 2019 ], "xaxis": "x", "y": [ 1286103 ], "yaxis": "y" }, { "alignmentgroup": "True", "customdata": [ [ 22.68613505518241 ] ], "hovertemplate": "event_year=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "2021", "marker": { "color": "purple", "pattern": { "shape": "" } }, "name": "2021", "offsetgroup": "2021", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ 2021 ], "xaxis": "x", "y": [ 377380 ], "yaxis": "y" } ], "layout": { "barmode": "relative", "legend": { "title": { "text": "event_year" }, "tracegroupgap": 0 }, "margin": { "t": 60 }, "showlegend": false, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", "linecolor": "#C8D4E3", "minorgridcolor": "#C8D4E3", 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"hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "white", "polar": { "angularaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" }, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Event Year EDA" }, "xaxis": { "anchor": "y", "categoryarray": [ 2019, 2021 ], "categoryorder": "array", "domain": [ 0, 1 ], "title": { "text": "Event Year" }, "type": "category" }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Total Number of Events" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "df_event_year = pd.DataFrame(raw_data.groupby(\"event_year\")[\"total_number_events\"].sum())\n", "df_event_year.reset_index(inplace=True)\n", "df_event_year[\"pct_total\"] = (df_event_year[\"total_number_events\"]/raw_data[\"total_number_events\"].sum())*100\n", "\n", "#interactive plotly bar chart\n", "fig_event_year = px.bar(data_frame = df_event_year, \n", " x = 'event_year', \n", " y = 'total_number_events',\n", " color_discrete_map={\n", " 2019: 'darkgray',\n", " 2021: 'purple'},\n", " color = 'event_year',\n", " hover_data = ['pct_total'])\n", "fig_event_year.update_xaxes(title_text = 'Event Year', type='category')\n", "fig_event_year.update_layout(\n", " title = 'Event Year EDA',\n", " yaxis_title = 'Total Number of Events',\n", " showlegend=False)\n", "fig_event_year.show()" ] }, { "cell_type": "markdown", "id": "38846d88", "metadata": {}, "source": [ "- *More than a third of the events were from 2019 and less than a quarter are from 2020*" ] }, { "cell_type": "code", "execution_count": 20, "id": "ef6c8371", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fe14792eb7b34234b9ca2525d6e810b3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "dt.show_grid(df_event_year, precision = 2)" ] }, { "cell_type": "markdown", "id": "ba4da471", "metadata": {}, "source": [ "#### Event Month Day EDA" ] }, { "cell_type": "code", "execution_count": 21, "id": "12d4ebd1", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "darkgray" }, "mode": "lines+markers", "name": "2019", "type": "scatter", "x": [ "Jan 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"#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Event Month Day EDA" }, "xaxis": { "title": { "text": "Date" } }, "yaxis": { "title": { "text": "Total Number of Events" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "raw_data_2019 = pd.DataFrame(raw_data[raw_data[\"event_year\"] == 2019])\n", "raw_data_2021 = pd.DataFrame(raw_data[raw_data[\"event_year\"] == 2021])\n", "\n", "#2019\n", "df_event_mday_2019 = pd.DataFrame(raw_data_2019.groupby(\"event_mday\")[\"total_number_events\"].sum())\n", "df_event_mday_2019.rename(columns = {\"total_number_events\" : \"mday_2019_total\"}, inplace = True)\n", "\n", "#2021\n", "df_event_mday_2021 = pd.DataFrame(raw_data_2021.groupby(\"event_mday\")[\"total_number_events\"].sum())\n", "df_event_mday_2021.rename(columns = {\"total_number_events\" : \"mday_2021_total\"}, inplace = True)\n", "\n", "#join dataframes\n", "df_event_mday = df_event_mday_2019.join(df_event_mday_2021)\n", "df_event_mday.reset_index(inplace=True)\n", "\n", "#interactive plotly line chart\n", "fig_event_mday = go.Figure()\n", "fig_event_mday.add_trace(go.Scatter(\n", " x = df_event_mday['event_mday'], \n", " y = df_event_mday['mday_2019_total'], \n", " name = '2019',\n", " mode = 'lines+markers',\n", " line=go.scatter.Line(color=\"darkgray\")))\n", "fig_event_mday.add_trace(go.Scatter(\n", " x = df_event_mday['event_mday'], \n", " y = df_event_mday['mday_2021_total'],\n", " name = '2021',\n", " mode = 'lines+markers',\n", " line=go.scatter.Line(color=\"purple\")))\n", "\n", "fig_event_mday.update_layout(\n", " title = 'Event Month Day EDA',\n", " xaxis_title = 'Date',\n", " yaxis_title = 'Total Number of Events')\n", "fig_event_mday.show()" ] }, { "cell_type": "markdown", "id": "7eb6e0ae", "metadata": {}, "source": [ "- _Based on day on day comparison, 2019 saw some dips on certain dates like Jan 05, Jan 12, etc._\n", "\n", "- _2020 also exhibits certain dips on total events like Jan 10, Jan 17, etc._" ] }, { "cell_type": "code", "execution_count": 22, "id": "a6833522", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a09584f513ae465ba7d590dd3b3d8fe6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "df_event_mday[\"diff_val\"] = df_event_mday[\"mday_2021_total\"] - df_event_mday[\"mday_2019_total\"]\n", "df_event_mday[\"diff_pct\"] = (df_event_mday[\"diff_val\"] / df_event_mday[\"mday_2019_total\"])*100\n", "dt.show_grid(df_event_mday, precision = 2)" ] }, { "cell_type": "markdown", "id": "a7ee3781", "metadata": {}, "source": [ "#### Event Weekday EDA" ] }, { "cell_type": "code", "execution_count": 23, "id": "dfac1b29", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": "darkgray" }, "name": "2019", "type": "bar", "x": [ "Sun", "Mon", "Tue", "Wed", "Thu", 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"linecolor": "#EBF0F8", "ticks": "" }, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Event Weekday EDA" }, "xaxis": { "title": { "text": "Day" } }, "yaxis": { "title": { "text": "Total Number of Events" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#2019\n", "df_event_wday_2019 = pd.DataFrame(raw_data_2019.groupby(\"event_wday\")[\"total_number_events\"].sum())\n", "df_event_wday_2019.rename(columns = {\"total_number_events\" : \"wday_2019_total\"}, inplace = True)\n", "\n", "#2021\n", "df_event_wday_2021 = pd.DataFrame(raw_data_2021.groupby(\"event_wday\")[\"total_number_events\"].sum())\n", "df_event_wday_2021.rename(columns = {\"total_number_events\" : \"wday_2021_total\"}, inplace = True)\n", "\n", "#join dataframes\n", "df_event_wday = df_event_wday_2019.join(df_event_wday_2021)\n", "df_event_wday.reset_index(inplace=True)\n", "df_event_wday.head(7)\n", "\n", "#interactive plotly bar chart\n", "fig_event_wday = go.Figure()\n", "fig_event_wday.add_trace(go.Bar(\n", " x = df_event_wday['event_wday'], \n", " y = df_event_wday['wday_2019_total'], \n", " name = '2019',\n", " marker=dict(color=\"darkgray\")))\n", "fig_event_wday.add_trace(go.Bar(\n", " x = df_event_wday['event_wday'], \n", " y = df_event_wday['wday_2021_total'], \n", " name = '2021',\n", " marker=dict(color=\"purple\")))\n", "fig_event_wday.update_layout(\n", " title = 'Event Weekday EDA',\n", " xaxis_title = 'Day',\n", " yaxis_title = 'Total Number of Events')\n", "fig_event_wday.show()" ] }, { "cell_type": "markdown", "id": "d09345f6", "metadata": {}, "source": [ "- _Viewing from a day of the week, there are less total events on Saturday and Sunday for 2019 while it is fairly consistent throughout the week with minimal fluctuations for 2020._" ] }, { "cell_type": "code", "execution_count": 24, "id": "badc106c", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bd055261448f406097791d0743895f99", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "df_event_wday[\"diff_val\"] = df_event_wday[\"wday_2021_total\"] - df_event_wday[\"wday_2019_total\"]\n", "df_event_wday[\"diff_pct\"] = (df_event_wday[\"diff_val\"] / df_event_wday[\"wday_2019_total\"])*100\n", "dt.show_grid(df_event_wday, precision = 2)" ] }, { "cell_type": "markdown", "id": "ce2ca836", "metadata": {}, "source": [ "#### Activity Group EDA" ] }, { "cell_type": "code", "execution_count": 25, "id": "fc82dff9", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "customdata": [ [ 7.730466737562091 ] ], "hovertemplate": "activity_group=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "Booking", "marker": { "color": "purple", "pattern": { "shape": "" } }, "name": "Booking", "offsetgroup": "Booking", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "Booking" ], "xaxis": "x", "y": [ 128595 ], "yaxis": "y" }, { "alignmentgroup": "True", "customdata": [ [ 92.2695332624379 ] ], "hovertemplate": "activity_group=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "Searching", "marker": { "color": "darkgray", "pattern": { "shape": "" } }, "name": "Searching", "offsetgroup": "Searching", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "Searching" ], "xaxis": "x", "y": [ 1534888 ], "yaxis": "y" } ], "layout": { "barmode": "relative", "legend": { "title": { "text": "activity_group" }, "tracegroupgap": 0 }, "margin": { "t": 60 }, "showlegend": false, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", "linecolor": "#C8D4E3", "minorgridcolor": "#C8D4E3", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", "linecolor": "#C8D4E3", "minorgridcolor": "#C8D4E3", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, 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0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, 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"#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "white", "polar": { "angularaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" }, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Activity Group EDA" }, "xaxis": { "anchor": "y", "categoryarray": [ "Booking", "Searching" ], "categoryorder": "array", "domain": [ 0, 1 ], "title": { "text": "Activity Group" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Total Number of Events" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "df_activity = pd.DataFrame(raw_data.groupby(\"activity_group\")[\"total_number_events\"].sum())\n", "df_activity.reset_index(inplace=True)\n", "df_activity[\"pct_total\"] = (df_activity[\"total_number_events\"]/raw_data[\"total_number_events\"].sum())*100\n", "\n", "#interactive plotly bar chart\n", "fig_activity = px.bar(data_frame = df_activity, \n", " x = 'activity_group', \n", " y = 'total_number_events',\n", " color_discrete_map={\n", " 'Booking' : 'purple',\n", " 'Searching': 'darkgray'},\n", " color = 'activity_group',\n", " hover_data = ['pct_total'])\n", "fig_activity.update_layout(\n", " title = 'Activity Group EDA',\n", " xaxis_title = 'Activity Group',\n", " yaxis_title = 'Total Number of Events',\n", " showlegend=False)\n", "fig_activity.show()" ] }, { "cell_type": "markdown", "id": "0b968a82", "metadata": {}, "source": [ "- _From the total number of events, only less than 8% are doing actual reservation. It would be interesting to see what the are the factors that affect the conversion from ‘Searching’ to ‘Booking’._\n", "\n", "- _An ‘conversion’ attribute can be use as a target variable for a prediction model._" ] }, { "cell_type": "code", "execution_count": 26, "id": "bd76eafa", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "eb153cb59d8647d48c679b704ad492d5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "dt.show_grid(df_activity, precision = 2)" ] }, { "cell_type": "markdown", "id": "e1a643bd", "metadata": {}, "source": [ "#### Reason for Travel Detailed EDA" ] }, { "cell_type": "code", "execution_count": 27, "id": "98202a24", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "customdata": [ [ 50.04553698474826 ] ], "hovertemplate": "reason_for_travel_detailed=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "Leisure - Non-Family", "marker": { "color": "orchid", "pattern": { "shape": "" } }, "name": "Leisure - Non-Family", "offsetgroup": "Leisure - Non-Family", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "Leisure - Non-Family" ], "xaxis": "x", "y": [ 832499 ], "yaxis": "y" }, { "alignmentgroup": "True", "customdata": [ [ 44.77538994988227 ] ], "hovertemplate": "reason_for_travel_detailed=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "Business", "marker": { "color": "darkorchid", "pattern": { "shape": "" } }, "name": "Business", "offsetgroup": "Business", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "Business" ], "xaxis": "x", "y": [ 744831 ], "yaxis": "y" }, { "alignmentgroup": "True", "customdata": [ [ 5.179073065369469 ] ], "hovertemplate": "reason_for_travel_detailed=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "Leisure - Family", "marker": { "color": "purple", "pattern": { "shape": "" } }, "name": "Leisure - Family", "offsetgroup": "Leisure - Family", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "Leisure - Family" ], "xaxis": "x", "y": [ 86153 ], "yaxis": "y" } ], "layout": { "barmode": "relative", "legend": { "title": { "text": "reason_for_travel_detailed" }, "tracegroupgap": 0 }, "margin": { "t": 60 }, "showlegend": false, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", "linecolor": "#C8D4E3", "minorgridcolor": "#C8D4E3", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", "linecolor": "#C8D4E3", "minorgridcolor": "#C8D4E3", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 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"#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "white", "polar": { "angularaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" }, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Reason for Travel Detailed EDA" }, "xaxis": { "anchor": "y", "categoryarray": [ "Leisure - Non-Family", "Business", "Leisure - Family" ], "categoryorder": "array", "domain": [ 0, 1 ], "title": { "text": "Reason for Travel" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Total Number of Events" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "df_reason_travel = pd.DataFrame(raw_data.groupby(\"reason_for_travel_detailed\")[\"total_number_events\"].sum())\n", "df_reason_travel.reset_index(inplace=True)\n", "df_reason_travel[\"pct_total\"] = (df_reason_travel[\"total_number_events\"]/raw_data[\"total_number_events\"].sum())*100\n", "df_reason_travel.sort_values(by = \"total_number_events\", ascending = False, inplace = True)\n", "\n", "#interactive plotly bar chart\n", "fig_reason_travel = px.bar(data_frame = df_reason_travel, \n", " x = 'reason_for_travel_detailed', \n", " y = 'total_number_events',\n", " color_discrete_map={\n", " 'Leisure - Non-Family': 'orchid',\n", " 'Business' : 'darkorchid',\n", " 'Leisure - Family': 'purple'},\n", " color = 'reason_for_travel_detailed',\n", " hover_data = ['pct_total'])\n", "fig_reason_travel.update_layout(\n", " title = 'Reason for Travel Detailed EDA',\n", " xaxis_title = 'Reason for Travel',\n", " yaxis_title = 'Total Number of Events',\n", " showlegend=False)\n", "fig_reason_travel.show()" ] }, { "cell_type": "markdown", "id": "707409d9", "metadata": {}, "source": [ "- _Almost 95% of the total events are from Leisure - Non-Family and Business. Although Leisure - Family accounts for more than 5%, it would be good to know the spending patterns._\n", "\n", "- _Intuitively, leisure travel consisting of 3 or more people for Leisure-Family category will spend more in the hotel services like rooms and food than Leisure - Non-Family and Business._\n", "\n" ] }, { "cell_type": "code", "execution_count": 28, "id": "4ad729c9", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9372de1adcb7473ab93c58913c502c57", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "dt.show_grid(df_reason_travel, precision = 2)" ] }, { "cell_type": "markdown", "id": "c915a3f7", "metadata": {}, "source": [ "#### Origin Country Code EDA" ] }, { "cell_type": "code", "execution_count": 29, "id": "75441fd5", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "customdata": [ [ 31.951153092637558 ], [ 22.844237061635138 ], [ 17.27129162125492 ], [ 10.42583543083999 ], [ 9.601901552345291 ], [ 5.293050785610673 ], [ 0.4464127376113853 ], [ 0.2177359191527656 ], [ 0.14571835119445165 ], [ 0.13165148065835358 ] ], "hovertemplate": "total_number_events=%{x}
origin_country_code=%{y}
pct_total=%{customdata[0]}", "legendgroup": "", "marker": { "color": "purple", "pattern": { "shape": "" } }, "name": "", "offsetgroup": "", "orientation": "h", "showlegend": false, "textposition": "auto", "type": "bar", "x": [ 531502, 380010, 287305, 173432, 159726, 88049, 7426, 3622, 2424, 2190 ], "xaxis": "x", "y": [ "GB", "DE", "FR", "US", "ES", "IT", "CA", "NL", "IN", "CH" ], "yaxis": "y" } ], "layout": { "barmode": "relative", "legend": { "tracegroupgap": 0 }, "margin": { "t": 60 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", "linecolor": 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"ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, 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"#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "white", "polar": { "angularaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" }, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Top 10 Origin Country Code EDA" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Total Number of Events" } }, "yaxis": { "anchor": "x", "categoryorder": "total ascending", "domain": [ 0, 1 ], "title": { "text": "Origin Country" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "df_origin_country = pd.DataFrame(raw_data.groupby(\"origin_country_code\", dropna=False)[\"total_number_events\"].sum())\n", "df_origin_country.reset_index(inplace=True)\n", "df_origin_country[\"pct_total\"] = (df_origin_country[\"total_number_events\"]/raw_data[\"total_number_events\"].sum())*100\n", "df_origin_country.sort_values(by = \"total_number_events\",ascending = False,inplace = True)\n", "\n", "df_origin_country_10 = pd.DataFrame(df_origin_country.head(10))\n", "\n", "#interactive plotly bar chart\n", "fig_origin_country = px.bar(data_frame = df_origin_country_10, \n", " y = 'origin_country_code', \n", " x = 'total_number_events',\n", " hover_data = ['pct_total'])\n", "fig_origin_country.update_traces(marker_color='purple')\n", "fig_origin_country.update_yaxes(categoryorder='total ascending')\n", "fig_origin_country.update_layout(\n", " title = 'Top 10 Origin Country Code EDA',\n", " yaxis_title = 'Origin Country',\n", " xaxis_title = 'Total Number of Events')\n", "fig_origin_country.show()" ] }, { "cell_type": "markdown", "id": "55516108", "metadata": {}, "source": [ "- _The top 5 origin country contributes to more than 92% of the total events exceeding the 80/20 Pareto principle._\n", "\n" ] }, { "cell_type": "code", "execution_count": 30, "id": "a17510e1", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b95e74493c5247aea702d77de288bee6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "dt.show_grid(df_origin_country, precision = 2)" ] }, { "cell_type": "markdown", "id": "f5cfe16f", "metadata": {}, "source": [ "#### Hotel City EDA" ] }, { "cell_type": "code", "execution_count": 31, "id": "f0b77e24", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "customdata": [ [ 19.775194576680374 ], [ 7.32054370258067 ], [ 5.826088995198629 ], [ 5.466482074057866 ], [ 5.406667816863773 ], [ 5.265999111502793 ], [ 3.67626239642966 ], [ 3.6709121764394346 ], [ 3.2596065003369437 ], [ 3.148694636494632 ] ], "hovertemplate": "total_number_events=%{x}
hotel_city=%{y}
pct_total=%{customdata[0]}", "legendgroup": "", "marker": { "color": "purple", "pattern": { "shape": "" } }, "name": "", "offsetgroup": "", "orientation": "h", "showlegend": false, "textposition": "auto", "type": "bar", "x": [ 328957, 121776, 96916, 90934, 89939, 87599, 61154, 61065, 54223, 52378 ], "xaxis": "x", "y": [ "london", "berlin", "madrid", "barcelona", "hamburg", "birmingham", "edinburgh", "manchester", "rome", "lyon" ], "yaxis": "y" } ], "layout": { "barmode": "relative", "legend": { "tracegroupgap": 0 }, "margin": { "t": 60 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": 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"showland": true, "subunitcolor": "#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "white", "polar": { "angularaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" }, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Top 10 Hotel City EDA" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Total Number of Events" } }, "yaxis": { "anchor": "x", "categoryorder": "total ascending", "domain": [ 0, 1 ], "title": { "text": "Hotel City" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "df_hotel_city = pd.DataFrame(raw_data.groupby(\"hotel_city\",dropna=False)[\"total_number_events\"].sum())\n", "df_hotel_city.reset_index(inplace=True)\n", "df_hotel_city[\"pct_total\"] = (df_hotel_city[\"total_number_events\"]/raw_data[\"total_number_events\"].sum())*100\n", "df_hotel_city.sort_values(by = \"total_number_events\",ascending = False,inplace = True)\n", "\n", "df_hotel_city_10 = pd.DataFrame(df_hotel_city.head(10))\n", "\n", "#interactive plotly bar chart\n", "fig_hotel_city = px.bar(data_frame = df_hotel_city_10, \n", " y = 'hotel_city', \n", " x = 'total_number_events',\n", " hover_data = ['pct_total'])\n", "fig_hotel_city.update_traces(marker_color='purple')\n", "fig_hotel_city.update_yaxes(categoryorder='total ascending')\n", "fig_hotel_city.update_layout(\n", " title = 'Top 10 Hotel City EDA',\n", " yaxis_title = 'Hotel City',\n", " xaxis_title = 'Total Number of Events')\n", "fig_hotel_city.show()" ] }, { "cell_type": "code", "execution_count": 32, "id": "642c078c", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f418af08a1ec4e88bd98b73bbfb82f1e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "dt.show_grid(df_hotel_city, precision = 2)" ] }, { "cell_type": "markdown", "id": "47f4d364", "metadata": {}, "source": [ "#### Hotel State EDA" ] }, { "cell_type": "code", "execution_count": 33, "id": "2f85e412", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "customdata": [ [ 2.7904703564749385 ], [ 0.77914832913832 ], [ 0.628079757953643 ], [ 0.399583283989076 ], [ 0.2717190377058257 ], [ 0.2445471339352431 ], [ 0.22242487599813165 ], [ 0.1814265610168544 ], [ 0.14860386309929227 ], [ 0.12239379663032324 ] ], "hovertemplate": "total_number_events=%{x}
hotel_state=%{y}
pct_total=%{customdata[0]}", "legendgroup": "", "marker": { "color": "purple", "pattern": { "shape": "" } }, "name": "", "offsetgroup": "", "orientation": "h", "showlegend": false, "textposition": "auto", "type": "bar", "x": [ 46419, 12961, 10448, 6647, 4520, 4068, 3700, 3018, 2472, 2036 ], "xaxis": "x", "y": [ "al", "ct", "sc", "ky", "nh", "on", "fl", "va", "tn", "provence-alpes-cte d'azur" ], "yaxis": "y" } ], "layout": { "barmode": "relative", "legend": { "tracegroupgap": 0 }, "margin": { "t": 60 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", 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"#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "white", "polar": { "angularaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" }, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Top 10 Hotel State EDA" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Total Number of Events" } }, "yaxis": { "anchor": "x", "categoryorder": "total ascending", "domain": [ 0, 1 ], "title": { "text": "Hotel State" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "df_hotel_state = pd.DataFrame(raw_data.groupby(\"hotel_state\",dropna=False)[\"total_number_events\"].sum())\n", "df_hotel_state.reset_index(inplace=True)\n", "\n", "#93.37% are mising values\n", "df_hotel_state[\"pct_total\"] = (df_hotel_state[\"total_number_events\"]/raw_data[\"total_number_events\"].sum())*100\n", "df_hotel_state.sort_values(by = \"total_number_events\", ascending = False,inplace = True)\n", "\n", "df_hotel_state_10 = pd.DataFrame(df_hotel_state.head(10))\n", "\n", "#interactive plotly bar chart\n", "fig_hotel_state = px.bar(data_frame = df_hotel_state_10, \n", " y = 'hotel_state', \n", " x = 'total_number_events',\n", " hover_data = ['pct_total'])\n", "fig_hotel_state.update_traces(marker_color='purple')\n", "fig_hotel_state.update_yaxes(categoryorder='total ascending')\n", "fig_hotel_state.update_layout(\n", " title = 'Top 10 Hotel State EDA',\n", " yaxis_title = 'Hotel State',\n", " xaxis_title = 'Total Number of Events')\n", "fig_hotel_state.show()" ] }, { "cell_type": "code", "execution_count": 34, "id": "1d27f6d2", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "088bd4dea76241b99492d6505f773478", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "dt.show_grid(df_hotel_state, precision = 2)" ] }, { "cell_type": "markdown", "id": "f20c86e6", "metadata": {}, "source": [ "#### Hotel Country EDA" ] }, { "cell_type": "code", "execution_count": 35, "id": "872c80b1", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "customdata": [ [ 33.76078986079209 ], [ 24.772720851370288 ], [ 14.049437235006309 ], [ 13.233678973575325 ], [ 7.9689422735308995 ], [ 5.075795785108714 ], [ 0.2699155927653003 ], [ 0.0028855119048406264 ], [ 0.0013225262897186205 ], [ 0.001082066964315235 ] ], "hovertemplate": "total_number_events=%{x}
hotel_country=%{y}
pct_total=%{customdata[0]}", "legendgroup": "", "marker": { "color": "purple", "pattern": { "shape": "" } }, "name": "", "offsetgroup": "", "orientation": "h", "showlegend": false, "textposition": "auto", "type": "bar", "x": [ 561605, 412090, 233710, 220140, 132562, 84435, 4490, 48, 22, 18 ], "xaxis": "x", "y": [ "gb", "de", "es", "fr", "it", "us", "ca", "espaa", "au", "on" ], "yaxis": "y" } ], "layout": { "barmode": "relative", "legend": { "tracegroupgap": 0 }, "margin": { "t": 60 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", "linecolor": 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"ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, 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"#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "white", "polar": { "angularaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" }, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Top 10 Hotel Country EDA" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Total Number of Events" } }, "yaxis": { "anchor": "x", "categoryorder": "total ascending", "domain": [ 0, 1 ], "title": { "text": "Hotel Country" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "df_hotel_country = pd.DataFrame(raw_data.groupby(\"hotel_country\",dropna=False)[\"total_number_events\"].sum())\n", "df_hotel_country.reset_index(inplace=True)\n", "\n", "#0.86% are mising values\n", "df_hotel_country[\"pct_total\"] = (df_hotel_country[\"total_number_events\"]/raw_data[\"total_number_events\"].sum())*100\n", "df_hotel_country.sort_values(by = \"total_number_events\", ascending = False,inplace = True)\n", "\n", "df_hotel_country_10 = pd.DataFrame(df_hotel_country.head(10))\n", "\n", "#interactive plotly bar chart\n", "fig_hotel_country = px.bar(data_frame = df_hotel_country_10, \n", " y = 'hotel_country', \n", " x = 'total_number_events',\n", " hover_data = ['pct_total'])\n", "fig_hotel_country.update_traces(marker_color='purple')\n", "fig_hotel_country.update_yaxes(categoryorder='total ascending')\n", "fig_hotel_country.update_layout(\n", " title = 'Top 10 Hotel Country EDA',\n", " yaxis_title = 'Hotel Country',\n", " xaxis_title = 'Total Number of Events')\n", "fig_hotel_country.show()" ] }, { "cell_type": "code", "execution_count": 36, "id": "6d850a6f", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dc3a4aef426345ce9dba9b2571248647", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "dt.show_grid(df_hotel_country, precision = 2)" ] }, { "cell_type": "markdown", "id": "52694525", "metadata": {}, "source": [ "- *London, GB accounts to almost 20% of the total number of events*\n", "\n", "- *As noted in the Data Dictionary, hotel_city, hotel_state, and hotel_country are raw data from the source systems and requires data cleaning to be useable.*" ] }, { "cell_type": "markdown", "id": "baeb10e7", "metadata": {}, "source": [ "#### Traveler Value Group EDA" ] }, { "cell_type": "code", "execution_count": 37, "id": "5d796357", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "customdata": [ [ 50.04553698474826 ] ], "hovertemplate": "reason_for_travel_detailed=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "Leisure - Non-Family", "marker": { "color": "orchid", "pattern": { "shape": "" } }, "name": "Leisure - Non-Family", "offsetgroup": "Leisure - Non-Family", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "Leisure - Non-Family" ], "xaxis": "x", "y": [ 832499 ], "yaxis": "y" }, { "alignmentgroup": "True", "customdata": [ [ 44.77538994988227 ] ], "hovertemplate": "reason_for_travel_detailed=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "Business", "marker": { "color": "darkorchid", "pattern": { "shape": "" } }, "name": "Business", "offsetgroup": "Business", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "Business" ], "xaxis": "x", "y": [ 744831 ], "yaxis": "y" }, { "alignmentgroup": "True", "customdata": [ [ 5.179073065369469 ] ], "hovertemplate": "reason_for_travel_detailed=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "Leisure - Family", "marker": { "color": "purple", "pattern": { "shape": "" } }, "name": "Leisure - Family", "offsetgroup": "Leisure - Family", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "Leisure - Family" ], "xaxis": "x", "y": [ 86153 ], "yaxis": "y" } ], "layout": { "barmode": "relative", "legend": { "title": { "text": "reason_for_travel_detailed" }, "tracegroupgap": 0 }, "margin": { "t": 60 }, "showlegend": false, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", 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"#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "white", "polar": { "angularaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" }, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Traveler Value Group EDA" }, "xaxis": { "anchor": "y", "categoryarray": [ "Leisure - Non-Family", "Business", "Leisure - Family" ], "categoryorder": "array", "domain": [ 0, 1 ], "title": { "text": "Traveler Value" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Total Number of Events" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "df_traveler_value = pd.DataFrame(raw_data.groupby(\"traveler_value_group\")[\"total_number_events\"].sum())\n", "df_traveler_value.reset_index(inplace=True)\n", "df_traveler_value[\"pct_total\"] = (df_traveler_value[\"total_number_events\"]/raw_data[\"total_number_events\"].sum())*100\n", "df_traveler_value.sort_values(by = \"total_number_events\", ascending = False, inplace = True)\n", "\n", "#interactive plotly bar chart\n", "fig_traveler_value = px.bar(data_frame = df_traveler_value, \n", " x = 'traveler_value_group', \n", " y = 'total_number_events',\n", " color_discrete_map={\n", " 'Low - Non-Family': 'orchid',\n", " 'Med' : 'darkorchid',\n", " 'High - Family': 'purple'},\n", " color = 'traveler_value_group',\n", " hover_data = ['pct_total'])\n", "fig_reason_travel.update_layout(\n", " title = 'Traveler Value Group EDA',\n", " xaxis_title = 'Traveler Value',\n", " yaxis_title = 'Total Number of Events',\n", " showlegend=False)\n", "fig_reason_travel.show()" ] }, { "cell_type": "markdown", "id": "6919ad92", "metadata": {}, "source": [ "- *Similar to reason_for_travel_detailed, it would be good to know the spending patterns of different Traveler Value Group.*\n", "\n", "- *Intuitively, “High” will spend more in the hotel services like rooms, food, and loyalty. Therefore, they require the best quality of customer care.*" ] }, { "cell_type": "code", "execution_count": 38, "id": "f95ae129", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e944bf4242f4444ba78bb39243049310", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "dt.show_grid(df_traveler_value, precision = 2)" ] }, { "cell_type": "markdown", "id": "7dca5b5e", "metadata": {}, "source": [ "#### Checkin Year EDA" ] }, { "cell_type": "code", "execution_count": 39, "id": "89695371", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "customdata": [ [ 77.1832354162922 ] ], "hovertemplate": "checkin_year=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "2019", "marker": { "color": "darkgray", "pattern": { "shape": "" } }, "name": "2019", "offsetgroup": "2019", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ 2019 ], "xaxis": "x", "y": [ 1283930 ], "yaxis": "y" }, { "alignmentgroup": "True", "customdata": [ [ 0.13056941369403835 ] ], "hovertemplate": "checkin_year=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "2020", "marker": { "color": "darkgray", "pattern": { "shape": "" } }, "name": "2020", "offsetgroup": "2020", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ 2020 ], "xaxis": "x", "y": [ 2172 ], "yaxis": "y" }, { "alignmentgroup": "True", "customdata": [ [ 22.607625085438205 ] ], "hovertemplate": "checkin_year=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "2021", "marker": { "color": "purple", "pattern": { "shape": "" } }, "name": "2021", "offsetgroup": "2021", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ 2021 ], "xaxis": "x", "y": [ 376074 ], "yaxis": "y" }, { "alignmentgroup": "True", "customdata": [ [ 0.07844985491285454 ] ], "hovertemplate": "checkin_year=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "2022", "marker": { "color": "darkgray", "pattern": { "shape": "" } }, "name": "2022", "offsetgroup": "2022", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ 2022 ], "xaxis": "x", "y": [ 1305 ], "yaxis": "y" }, { "alignmentgroup": "True", "customdata": [ [ 6.011483135084639e-05 ] ], "hovertemplate": "checkin_year=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "2023", "marker": { "color": "darkgray", "pattern": { "shape": "" } }, "name": "2023", "offsetgroup": "2023", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ 2023 ], "xaxis": "x", "y": [ 1 ], "yaxis": "y" }, { "alignmentgroup": "True", "customdata": [ [ 6.011483135084639e-05 ] ], "hovertemplate": "checkin_year=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "2032", "marker": { "color": "darkgray", "pattern": { "shape": "" } }, "name": "2032", "offsetgroup": "2032", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ 2032 ], "xaxis": "x", "y": [ 1 ], "yaxis": "y" } ], "layout": { "barmode": "relative", "legend": { "title": { "text": "checkin_year" }, "tracegroupgap": 0 }, "margin": { "t": 60 }, "showlegend": false, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", "linecolor": "#C8D4E3", "minorgridcolor": "#C8D4E3", 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"hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "white", "polar": { "angularaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" }, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Checkin Year EDA" }, "xaxis": { "anchor": "y", "categoryarray": [ 2019, 2020, 2021, 2022, 2023, 2032 ], "categoryorder": "array", "domain": [ 0, 1 ], "title": { "text": "Checkin Year" }, "type": "category" }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Total Number of Events" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "df_checkin_year = pd.DataFrame(raw_data.groupby(\"checkin_year\")[\"total_number_events\"].sum())\n", "df_checkin_year.reset_index(inplace=True)\n", "df_checkin_year[\"pct_total\"] = (df_checkin_year[\"total_number_events\"]/raw_data[\"total_number_events\"].sum())*100\n", "\n", "#interactive plotly bar chart\n", "fig_checkin_year = px.bar(data_frame = df_checkin_year, \n", " x = 'checkin_year', \n", " y = 'total_number_events',\n", " color_discrete_map={\n", " 2019 : 'darkgray',\n", " 2020 : 'darkgray',\n", " 2021 : 'purple',\n", " 2022 : 'darkgray',\n", " 2023 : 'darkgray',\n", " 2032 : 'darkgray',},\n", " color = 'checkin_year',\n", " hover_data = ['pct_total'])\n", "fig_checkin_year.update_xaxes(title_text = 'Checkin Year', type='category')\n", "fig_checkin_year.update_layout(\n", " title = 'Checkin Year EDA',\n", " yaxis_title = 'Total Number of Events',\n", " showlegend=False)\n", "fig_checkin_year.show()" ] }, { "cell_type": "code", "execution_count": 40, "id": "f2a5ff7f", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "617dfc31ed01473f852be52e31f047ac", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "dt.show_grid(df_checkin_year, precision = 2)" ] }, { "cell_type": "markdown", "id": "553487e4", "metadata": {}, "source": [ "#### Checkin Month EDA" ] }, { "cell_type": "code", "execution_count": 41, "id": "4e86c03a", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "customdata": [ [ 34.66161060858452 ], [ 23.897448906901964 ], [ 11.928465755285748 ], [ 8.261941961534925 ], [ 6.074243019014922 ], [ 4.2585346528939585 ], [ 3.189873295969962 ], [ 2.5772430496734864 ], [ 2.021721893160315 ], [ 1.359496910999391 ], [ 0.7633982433243982 ], [ 1.0060217026564142 ] ], "hovertemplate": "checkin_month=%{x}
total_number_events=%{y}
pct_total=%{customdata[0]}", "legendgroup": "", "marker": { "color": "purple", "pattern": { "shape": "" } }, "name": "", "offsetgroup": "", "orientation": "v", "showlegend": false, "textposition": "auto", "type": "bar", "x": [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec" ], "xaxis": "x", "y": [ 576590, 397530, 198428, 137436, 101044, 70840, 53063, 42872, 33631, 22615, 12699, 16735 ], "yaxis": "y" } ], "layout": { "barmode": "relative", "legend": { "tracegroupgap": 0 }, "margin": { "t": 60 }, "showlegend": false, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", "linecolor": "#C8D4E3", "minorgridcolor": "#C8D4E3", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", "linecolor": "#C8D4E3", "minorgridcolor": "#C8D4E3", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, 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0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" 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"showlakes": true, "showland": true, "subunitcolor": "#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "white", "polar": { "angularaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" }, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Checkin Month EDA" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Checkin Month" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Total Number of Events" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "df_checkin_month = pd.DataFrame(raw_data.groupby(\"checkin_month\")[\"total_number_events\"].sum())\n", "df_checkin_month.reset_index(inplace=True)\n", "df_checkin_month[\"pct_total\"] = (df_checkin_month[\"total_number_events\"]/raw_data[\"total_number_events\"].sum())*100\n", "\n", "#interactive plotly bar chart\n", "fig_checkin_month = px.bar(data_frame = df_checkin_month, \n", " x = 'checkin_month', \n", " y = 'total_number_events',\n", " hover_data = ['pct_total'])\n", "fig_checkin_month.update_traces(marker_color='purple')\n", "fig_checkin_month.update_layout(\n", " title = 'Checkin Month EDA',\n", " xaxis_title = 'Checkin Month',\n", " yaxis_title = 'Total Number of Events',\n", " showlegend=False)\n", "fig_checkin_month.show()" ] }, { "cell_type": "code", "execution_count": 42, "id": "1a0281ee", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f890ad157afa4817b570f170ac192c01", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "dt.show_grid(df_checkin_month, precision = 2)" ] }, { "cell_type": "markdown", "id": "c04f4c9f", "metadata": {}, "source": [ "- _Most of the checkins are from 2019 and January. The data also show that there is a decreasing trend towards the end of the year. Probably, it would be good to have some promotions during the 3rd and 4th quarter to increase the number of events._" ] }, { "cell_type": "markdown", "id": "1a3cf033", "metadata": {}, "source": [ "#### Total Number of Events Histogram" ] }, { "cell_type": "code", "execution_count": 43, "id": "5aefabab", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "bingroup": "x", "hovertemplate": "total_number_events=%{x}
count=%{y}", "legendgroup": "", "marker": { "color": "purple", "pattern": { "shape": "" } }, "name": "", "nbinsx": 100, "offsetgroup": "", "orientation": "v", "showlegend": false, "type": "histogram", "x": [ 1, 1, 1, 2, 2, 1, 1, 1, 3, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 3, 1, 4, 2, 4, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 3, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 3, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 4, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 4, 2, 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"linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Total Number of Events Histogram" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Total Number of Events" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Count" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "df_total_event = pd.DataFrame(raw_data[\"total_number_events\"].value_counts())\n", "df_total_event.reset_index(inplace=True)\n", "df_total_event[\"pct_total\"] = (df_total_event[\"total_number_events\"]/raw_data[\"total_number_events\"].sum())*100\n", "df_total_event.rename(columns = {\"index\": \"total_number_events\", \"total_number_events\" : \"count\"}, inplace = True)\n", "df_total_event.sort_values(by = \"count\", ascending = False, inplace = True)\n", "\n", "fig_total_event = px.histogram(data_frame = raw_data,\n", " x='total_number_events',\n", " nbins = 100)\n", "fig_total_event.update_traces(marker_color='purple')\n", "fig_total_event.update_layout(\n", " title = 'Total Number of Events Histogram',\n", " yaxis_title = 'Count',\n", " xaxis_title = 'Total Number of Events',\n", " showlegend=False)\n", "fig_total_event.show()" ] }, { "cell_type": "code", "execution_count": 44, "id": "842106b5", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "edb0c7a218f54f6ea786d40d3f999031", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "dt.show_grid(df_total_event, precision = 2)" ] }, { "cell_type": "markdown", "id": "3e9a4baf", "metadata": {}, "source": [ "### 1.3 Data Cleaning\n", "\n", "#### **Clean Hotel City**" ] }, { "cell_type": "code", "execution_count": 45, "id": "4b3f907f", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e5635fde5ea14bdf85488932e10afadb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "clean_data = raw_data.copy(deep=True)\n", "\n", "#replace misspelled hotel city\n", "replace_map_hotel_city = {\"dusseldorf\" : \"duesseldorf\",\n", " \"firenze\" : \"florence\",\n", " \"kln\" : \"koeln\",\n", " \"mnchen\" : \"munich\",\n", " \"seville\" : \"sevilla\",\n", " \"venezia\": \"venice\"}\n", "clean_data[\"hotel_city\"].replace(replace_map_hotel_city, inplace = True)\n", "clean_data[\"hotel_city\"] = clean_data[\"hotel_city\"].astype(\"category\")\n", "\n", "#generate dataframe\n", "df_clean_hotel_city = pd.DataFrame(clean_data.groupby(\"hotel_city\",dropna=False)[\"total_number_events\"].sum())\n", "df_clean_hotel_city.reset_index(inplace=True)\n", "df_clean_hotel_city[\"pct_total\"] = (df_clean_hotel_city[\"total_number_events\"]/clean_data[\"total_number_events\"].sum())*100\n", "df_clean_hotel_city.sort_values(by = \"total_number_events\",ascending = False,inplace = True)\n", "\n", "#interactive data table\n", "dt.show_grid(df_clean_hotel_city, precision = 2)" ] }, { "cell_type": "markdown", "id": "eb54c9c0", "metadata": {}, "source": [ "**Merged the following cities:**\n", "- dusseldorf -> duesseldorf\n", "- firenze -> florence\n", "- kln -> koeln\n", "- mnchen -> munich\n", "- seville -> sevilla\n", "- venezia -> venice" ] }, { "cell_type": "markdown", "id": "6252c948", "metadata": {}, "source": [ "#### **Clean Hotel Country**" ] }, { "cell_type": "code", "execution_count": 46, "id": "60e1122d", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a0936d5b16d044508666c641f8b68bdb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "replace_map_hotel_country = {\"alemania\" : \"de\",\n", " \"allemagne\" : \"de\",\n", " \"germania\" : \"de\",\n", " \"espaa\" : \"es\",\n", " \"catalunya\" : \"es\",\n", " \"gran bretaa\": \"gb\",\n", " \"gran bretagna\" : \"gb\",\n", " \"grande bretagne\" : \"gb\",\n", " \"grobritannien\" : \"gb\",\n", " \"regno unito\" : \"gb\",\n", " \"reino unido\" : \"gb\",\n", " \"royaume-uni\" : \"gb\",\n", " \"sct\" : \"gb\",\n", " \"on\" : \"gb\", #london\n", " \"ns\" : \"gb\", #liverpool\n", " \"jp\" : \"fr\", #bordeaux\n", " \"br\" : \"es\", #barcelona\n", " \"co\" : \"es\"} #madrid\n", " \n", "clean_data[\"hotel_country\"].replace(replace_map_hotel_country, inplace = True)\n", "clean_data[\"hotel_country\"] = clean_data[\"hotel_country\"].astype(\"category\")\n", "\n", "#generate dataframe\n", "df_clean_hotel_country = pd.DataFrame(clean_data.groupby(\"hotel_country\",dropna=False)[\"total_number_events\"].sum())\n", "df_clean_hotel_country.reset_index(inplace=True)\n", "df_clean_hotel_country[\"pct_total\"] = (df_clean_hotel_country[\"total_number_events\"]/clean_data[\"total_number_events\"].sum())*100\n", "df_clean_hotel_country.sort_values(by = \"total_number_events\",ascending = False,inplace = True)\n", "\n", "#interactive data table\n", "dt.show_grid(df_clean_hotel_country, precision = 2)" ] }, { "cell_type": "markdown", "id": "1460abfa", "metadata": {}, "source": [ "**Issues:**\n", "\n", "- Merged and renamed countries. However US, CA, AU, and RU are still incorrect (does not match correctly with hotel_city)\n", "\n", "- There are missing countries and (not set) entries\n", "\n", "- Assuming the hotel_city is correct, a better approach is to map the hotel_country from the hotel_city\n", "\n", "- Additionally, hotel_state is unusable because of 93.37% of the data are missing. If this attribute is needed, hotel_state can be mapped from the hotel_city" ] }, { "cell_type": "markdown", "id": "20fc74ad", "metadata": {}, "source": [ "#### **Final Clean Data**" ] }, { "cell_type": "code", "execution_count": 47, "id": "3d68f5ce", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 724795 entries, 0 to 724794\n", "Data columns (total 14 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 event_date 724795 non-null datetime64[ns]\n", " 1 activity_group 724795 non-null category \n", " 2 reason_for_travel_detailed 724795 non-null category \n", " 3 origin_country_code 723546 non-null category \n", " 4 hotel_city 724795 non-null category \n", " 5 traveler_value_group 724210 non-null category \n", " 6 departure_checkin_date 724795 non-null datetime64[ns]\n", " 7 total_number_events 724795 non-null int64 \n", " 8 event_wday 724795 non-null category \n", " 9 event_year 724795 non-null category \n", " 10 event_mday 724795 non-null category \n", " 11 checkin_wday 724795 non-null category \n", " 12 checkin_year 724795 non-null category \n", " 13 checkin_month 724795 non-null category \n", "dtypes: category(11), datetime64[ns](2), int64(1)\n", "memory usage: 24.9 MB\n" ] } ], "source": [ "clean_data.drop(columns = [\"hotel_state\", \"hotel_country\"], inplace = True)\n", "clean_data.info()" ] }, { "cell_type": "markdown", "id": "5a07dde5", "metadata": {}, "source": [ "- For the final data to be used for analysis, hotel_state and hotel_country will be excluded since the final report only requires top destination per city.\n", "\n", "- Additionally, the outliers in the total_number_events and missing values from other fields will be kept as is. The analysis assumes that the data inputs are correct." ] }, { "cell_type": "markdown", "id": "601385f2", "metadata": {}, "source": [ "## 2. 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "clean_data_2019 = pd.DataFrame(clean_data[clean_data[\"event_year\"] == 2019])\n", "clean_data_2019_booking = pd.DataFrame(clean_data_2019[clean_data_2019[\"activity_group\"] == \"Booking\"])\n", "clean_data_2019_searching = pd.DataFrame(clean_data_2019[clean_data_2019[\"activity_group\"] == \"Searching\"])\n", "\n", "clean_data_2021 = pd.DataFrame(clean_data[clean_data[\"event_year\"] == 2021])\n", "clean_data_2021_booking = pd.DataFrame(clean_data_2021[clean_data_2021[\"activity_group\"] == \"Booking\"])\n", "clean_data_2021_searching = pd.DataFrame(clean_data_2021[clean_data_2021[\"activity_group\"] == \"Searching\"])\n", "\n", "#2019\n", "df_booking_2019 = pd.DataFrame(clean_data_2019_booking.groupby(\"hotel_city\")[\"total_number_events\"].sum())\n", "df_booking_2019.rename(columns = {\"total_number_events\" : \"booking_2019_total\"}, inplace = True)\n", "\n", "#2021\n", "df_booking_2021 = pd.DataFrame(clean_data_2021_booking.groupby(\"hotel_city\")[\"total_number_events\"].sum())\n", "df_booking_2021.rename(columns = {\"total_number_events\" : \"booking_2021_total\"}, inplace = True)\n", "\n", "#join dataframes\n", "df_booking_city = df_booking_2019.join(df_booking_2021)\n", "df_booking_city.reset_index(inplace=True)\n", "df_booking_city.sort_values(by = \"booking_2021_total\", ascending = False, inplace = True)\n", "\n", "#interactive plotly line chart\n", "fig_booking_city = go.Figure()\n", "fig_booking_city.add_trace(go.Scatter(\n", " x = df_booking_city['hotel_city'],\n", " y = df_booking_city['booking_2021_total'], \n", " name = '2021',\n", " mode = 'lines+markers',\n", " line=go.scatter.Line(color='purple')))\n", "fig_booking_city.add_trace(go.Scatter(\n", " x = df_booking_city['hotel_city'], \n", " y = df_booking_city['booking_2019_total'], \n", " name = '2019',\n", " mode = 'lines+markers',\n", " line=go.scatter.Line(color=\"darkgray\")))\n", "fig_booking_city.update_layout(\n", " title = 'Booking Top Destinations by City',\n", " xaxis_title = 'Booking Destination',\n", " yaxis_title = 'Total Number of Events')\n", "fig_booking_city.show()" ] }, { "cell_type": "code", "execution_count": 49, "id": "aa58ddc6", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a571391972b740cbbedf807f666a9f22", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "df_booking_city[\"diff_val\"] = df_booking_city[\"booking_2021_total\"] - df_booking_city[\"booking_2019_total\"]\n", "df_booking_city[\"diff_pct\"] = (df_booking_city[\"diff_val\"] / df_booking_city[\"booking_2019_total\"])*100\n", "df_booking_city.sort_values(by = \"diff_pct\", ascending = False, inplace = True)\n", "dt.show_grid(df_booking_city, precision = 2)" ] }, { "cell_type": "code", "execution_count": 50, "id": "14fa5740", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "diff_pct=%{x}
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15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Top 10 Origin Country" }, "xaxis": { "title": { "text": "Origin Country" } }, "yaxis": { "title": { "text": "Total Number of Events" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "#2019\n", "df_booking_2019_country = pd.DataFrame(clean_data_2019_booking.groupby(\"origin_country_code\")[\"total_number_events\"].sum())\n", "df_booking_2019_country.rename(columns = {\"total_number_events\" : \"booking_2019_country\"}, inplace = True)\n", "\n", "#2021\n", "df_booking_2021_country = pd.DataFrame(clean_data_2021_booking.groupby(\"origin_country_code\")[\"total_number_events\"].sum())\n", "df_booking_2021_country.rename(columns = {\"total_number_events\" : \"booking_2021_country\"}, inplace = True)\n", "\n", "#join dataframes\n", "df_booking_country = df_booking_2019_country.join(df_booking_2021_country)\n", "df_booking_country.reset_index(inplace=True)\n", "df_booking_country.sort_values(by = \"booking_2021_country\", ascending = False, inplace = True)\n", "\n", "df_booking_country_10 = pd.DataFrame(df_booking_country.head(10))\n", "\n", "#interactive plotly bar chart\n", "fig_booking_country = go.Figure()\n", "fig_booking_country.add_trace(go.Bar(\n", " x = df_booking_country_10['origin_country_code'],\n", " y = df_booking_country_10['booking_2021_country'], \n", " name = '2021',\n", " marker=dict(color=\"purple\")))\n", "fig_booking_country.add_trace(go.Bar(\n", " x = df_booking_country_10['origin_country_code'], \n", " y = df_booking_country_10['booking_2019_country'], \n", " name = '2019',\n", " marker=dict(color=\"darkgray\")))\n", "fig_booking_country.update_layout(\n", " title = 'Top 10 Origin Country',\n", " xaxis_title = 'Origin Country',\n", " yaxis_title = 'Total Number of Events')\n", "fig_booking_country.show()" ] }, { "cell_type": "code", "execution_count": 52, "id": "7aca5368", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0af4a6f089ea400e8b3a97c14a1803f4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "df_booking_country[\"diff_val\"] = df_booking_country[\"booking_2021_country\"] - df_booking_country[\"booking_2019_country\"]\n", "df_booking_country[\"diff_pct\"] = (df_booking_country[\"diff_val\"] / df_booking_country[\"booking_2019_country\"])*100\n", "df_booking_country.sort_values(by = \"booking_2021_country\", ascending = False, inplace = True)\n", "dt.show_grid(df_booking_country, precision = 2)" ] }, { "cell_type": "markdown", "id": "b0511111", "metadata": {}, "source": [ "- In terms of percent change of activity (2021 vs 2019), only florence has positive growth of 203.92%.\n", "\n", "- The rest of the destinations experienced negative percent change from venice (-29.69%) up to the worst hit cities which are leeds (-93.82%), frankfurt(-93.84%), koeln (-93.85%).\n", "\n", "- In terms of total number of events, birmingham has the highest events with 2,650 followed by london with 2,600.\n", "\n", "- The bottom 3 destination cities have less than 100 events which are malaga with 83, cologne with 52, and palma de mallorca with a mere 38.\n", "\n", "- The top origin countries from which bookings were made are US, FR, GB, DE, and ES." ] }, { "cell_type": "markdown", "id": "3f924bbf", "metadata": {}, "source": [ "### 2.1 Searching" ] }, { "cell_type": "code", "execution_count": 53, "id": "0e1eadef", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "purple" }, "mode": "lines+markers", "name": "2021", "type": "scatter", "x": [ "london", "birmingham", "barcelona", "florence", "madrid", "rome", "nice", "venice", "berlin", "manchester", "munich", "hamburg", "marseille", "milan", "lyon", "edinburgh", "bristol", "bordeaux", "sevilla", "lille", "frankfurt", "palma de mallorca", "orly", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "#2019\n", "df_searching_2019 = pd.DataFrame(clean_data_2019_searching.groupby(\"hotel_city\")[\"total_number_events\"].sum())\n", "df_searching_2019.rename(columns = {\"total_number_events\" : \"searching_2019_total\"}, inplace = True)\n", "\n", "#2021\n", "df_searching_2021 = pd.DataFrame(clean_data_2021_searching.groupby(\"hotel_city\")[\"total_number_events\"].sum())\n", "df_searching_2021.rename(columns = {\"total_number_events\" : \"searching_2021_total\"}, inplace = True)\n", "\n", "#join dataframes\n", "df_searching_city = df_searching_2019.join(df_searching_2021)\n", "df_searching_city.reset_index(inplace=True)\n", "df_searching_city.sort_values(by = \"searching_2021_total\", ascending = False, inplace = True)\n", "\n", "#interactive plotly line chart\n", "fig_searching_city = go.Figure()\n", "fig_searching_city.add_trace(go.Scatter(\n", " x = df_searching_city['hotel_city'],\n", " y = df_searching_city['searching_2021_total'], \n", " name = '2021',\n", " mode = 'lines+markers',\n", " line=go.scatter.Line(color='purple')))\n", "fig_searching_city.add_trace(go.Scatter(\n", " x = df_searching_city['hotel_city'], \n", " y = df_searching_city['searching_2019_total'], \n", " name = '2019',\n", " mode = 'lines+markers',\n", " line=go.scatter.Line(color=\"darkgray\")))\n", "fig_searching_city.update_layout(\n", " title = 'Searching Top Destinations by City',\n", " xaxis_title = 'Searching Destination',\n", " yaxis_title = 'Total Number of Events')\n", "fig_searching_city.show()" ] }, { "cell_type": "code", "execution_count": 54, "id": "153709f4", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "19a17c53dafa4e14ba0e78d00772de07", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "df_searching_city[\"diff_val\"] = df_searching_city[\"searching_2021_total\"] - df_searching_city[\"searching_2019_total\"]\n", "df_searching_city[\"diff_pct\"] = (df_searching_city[\"diff_val\"] / df_searching_city[\"searching_2019_total\"])*100\n", "df_searching_city.sort_values(by = \"diff_pct\", ascending = False, inplace = True)\n", "dt.show_grid(df_searching_city, precision = 2)" ] }, { "cell_type": "code", "execution_count": 55, "id": "84c67c8c", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "diff_pct=%{x}
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15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Top 10 Origin Country" }, "xaxis": { "title": { "text": "Origin Country" } }, "yaxis": { "title": { "text": "Total Number of Events" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate dataframe\n", "#2019\n", "df_searching_2019_country = pd.DataFrame(clean_data_2019_searching.groupby(\"origin_country_code\")[\"total_number_events\"].sum())\n", "df_searching_2019_country.rename(columns = {\"total_number_events\" : \"searching_2019_country\"}, inplace = True)\n", "\n", "#2021\n", "df_searching_2021_country = pd.DataFrame(clean_data_2021_booking.groupby(\"origin_country_code\")[\"total_number_events\"].sum())\n", "df_searching_2021_country.rename(columns = {\"total_number_events\" : \"searching_2021_country\"}, inplace = True)\n", "\n", "#join dataframes\n", "df_searching_country = df_searching_2019_country.join(df_searching_2021_country)\n", "df_searching_country.reset_index(inplace=True)\n", "df_searching_country.sort_values(by = \"searching_2021_country\", ascending = False, inplace = True)\n", "\n", "df_searching_country_10 = pd.DataFrame(df_searching_country.head(10))\n", "\n", "#interactive plotly bar chart\n", "fig_searching_country = go.Figure()\n", "fig_searching_country.add_trace(go.Bar(\n", " x = df_searching_country_10['origin_country_code'],\n", " y = df_searching_country_10['searching_2021_country'], \n", " name = '2021',\n", " marker=dict(color=\"purple\")))\n", "fig_searching_country.add_trace(go.Bar(\n", " x = df_searching_country_10['origin_country_code'], \n", " y = df_searching_country_10['searching_2019_country'], \n", " name = '2019',\n", " marker=dict(color=\"darkgray\")))\n", "fig_searching_country.update_layout(\n", " title = 'Top 10 Origin Country',\n", " xaxis_title = 'Origin Country',\n", " yaxis_title = 'Total Number of Events')\n", "fig_searching_country.show()" ] }, { "cell_type": "code", "execution_count": 57, "id": "5f1614ce", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7e7eac331d9d4938b99d487ddab97155", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#interactive data table\n", "df_searching_country[\"diff_val\"] = df_searching_country[\"searching_2021_country\"] - df_searching_country[\"searching_2019_country\"]\n", "df_searching_country[\"diff_pct\"] = (df_searching_country[\"diff_val\"] / df_searching_country[\"searching_2019_country\"])*100\n", "df_searching_country.sort_values(by = \"searching_2021_country\", ascending = False, inplace = True)\n", "dt.show_grid(df_searching_country, precision = 2)" ] }, { "cell_type": "markdown", "id": "32b80953", "metadata": {}, "source": [ "- In terms of percent change of activity (2021 vs 2019), florence, venince, and birmingham have positive percent change with values of 181.83%, 27.73% and 1.51% respectively.\n", "\n", "- The rest of the destinations experienced negative percent change from palma de mallorca (-33.84%) up to the worst hit cities which are koeln (-92.54%) and leeds (-93.70%)\n", "\n", "- In terms of total number of events, london has the highest events with 54,812 and consistently at the top for 2019 and 2021.\n", "\n", "- The bottom 4 destination cities have less than 2000 events which are malaga (1,836), cologne (1,817), hannover (1,573), and leeds (1,210).\n", "\n", "- The top origin countries from which bookings were made are US, FR, GB, ES, and DE." ] }, { "cell_type": "markdown", "id": "3d558d29", "metadata": {}, "source": [ "## 3. Actionable Recommendations\n", "\n", "- Add new attribute like a flag to understand the factors that affect the conversion from ‘Searching’ to ‘Booking’. An ‘conversion’ attribute can be use as a target variable for a prediction model.\n", "\n", "- Add spending patterns information to know how ‘Reason for Travel Detailed’ and ‘Traveler Value Group’ contribute to the bottom line.\n", "\n", "- Add user demographics information, time of event, and campaigns used to advertise the hotel to better plan and invest marketing and operational resources.\n", "\n", "- Suggest to focus the marketing and operational resources to the top origin countries from which bookings were made such as US, FR, GB, DE, and ES. Additionally, may need to investigate why there are no bookings or even search from US for 2019. This may be a data source issue.\n", "\n", "- Finally, aside from looking at the City Hotel, perform additional interactive visualization and data analysis to other attributes (reason_for_travel_detailed, traveler_value_group, and departure_checkin_date).\n", "\n" ] } ], "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": "3.8.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "0312ed5cb80b4a2fb2ac438ff8d6a18f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "088bd4dea76241b99492d6505f773478": { "model_module": "qgrid", "model_module_version": "^1.1.3", "model_name": "QgridModel", "state": { "_columns": { "hotel_state": { 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