关于数据分析:业务数据分析最佳案例旅游业数据分析⛵

本文应用『城市酒店和度假酒店的预订信息』，对旅游业的倒退现状进行数据分析，蕴含了残缺的数据分析流程：数据读取、数据初览、数据预处理、描述性统计、探索性数据分析、关联剖析、相关性剖析。

作者：韩信子@ShowMeAI
数据分析实战系列：https://www.showmeai.tech/tutorials/40
本文地址：https://www.showmeai.tech/article-detail/388
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在本篇内容中，ShowMeAI将带大家对旅游业，次要是酒店预订需要进行剖析，咱们应用到的数据集蕴含城市酒店和度假酒店的预订信息，包含预订工夫、住宿时长、客人入住的周末或工作日晚数以及可用停车位数量等信息。

咱们本次用到的是 酒店预订数据集，蕴含 119390 位客人，有 32 个特色字段，大家能够通过 ShowMeAI 的百度网盘地址下载。

实战数据集下载（百度网盘）：公✦众✦号『ShowMeAI钻研核心』回复『实战』，或者点击这里获取本文 [[59]旅游业大数据多维度业务剖析案例](https://www.showmeai.tech/art...) 『酒店预订数据集』
⭐ ShowMeAI官网GitHub：https://github.com/ShowMeAI-Hub

本文数据分析局部波及的工具库，大家能够参考ShowMeAI制作的工具库速查表和教程进行学习和疾速应用。
数据迷信工具库速查表 | Pandas 速查表
图解数据分析：从入门到精通系列教程

导入工具库

# 数据处理&科学计算import pandas as pdimport numpy as np# 数据分析&绘图import matplotlib.pyplot as pltimport seaborn as snsimport plotly.express as pximport plotly.graph_objects as goimport plotly.figure_factory as ffimport warningswarnings.filterwarnings("ignore")# 科学计算from scipy.stats import skew,kurtosisimport pylab as py# 工夫import timeimport datetimefrom datetime import datetimefrom datetime import date

读取数据

df = pd.read_csv("hotel_bookings.csv")df.head()

数据信息初览

df.info()

数据预处理

荡涤&缺失值解决

首先统计字段缺失值比例

df.isnull().sum().sort_values(ascending = False) / len(df)

咱们对有缺失的字段做一些缺失值填充工作

# 填充"agent" 和 "company" 字段中的缺失值df["agent"].fillna(0, inplace = True)df["company"].fillna(0, inplace = True)# 应用众数填充"country"字段缺失值df["country"].fillna(df["country"].mode()[0], inplace = True)# 删除蕴含"children"缺失值的数据记录df.dropna(subset = ["children"], axis = 0, inplace = True)

字段数据处理

# 将“distribution_channel”列中的“Undefined”转换为“TA/TO”df["distribution_channel"].replace("Undefined", "TA/TO", inplace = True)# meal字段映射解决df["meal"].replace(["Undefined", "BB", "FB", "HB", "SC" ], ["No Meal", "Breakfast", "Full Board", "Half Board", "No Meal"], inplace = True)# 将“is_canceled”列的值从 0 和 1 转换为“Cancelled”和“Not Cancelled”df["is_canceled"].replace([0, 1], ["Cancelled", "Not Cancelled"], inplace = True)

调整数据类型

将children、agent和company列的数据类型转换为整型
将reservation_status_date列的数据类型从对象转换为日期类型

# 转整型df["children"].astype(int)df["agent"].astype(int)df["company"].astype(int)# 工夫型pd.to_datetime(df["reservation_status_date"])

反复数据处理

df.drop_duplicates(inplace = True)

构建汇总字段

咱们对顾客总体的寓居晚数进行统计

df["total_nights"] = df["stays_in_weekend_nights"] + df["stays_in_week_nights"]

描述性统计

咱们基于pandas的简略性能，对数据的统计散布做一个解决理解

df.describe().T

探索性数据分析

酒店维度剖析

# 咱们对 城市酒店 和 度假酒店 进行统计分析labels = ['City Hotel', 'Resort Hotel']colors = ["#538B8B", "#7AC5CD"]order = df['hotel'].value_counts().indexplt.figure(figsize = (19, 9))plt.suptitle('Bookings By Hotels', fontweight = 'heavy', fontsize = '16',            fontfamily = 'sans-serif', color = "black")# Pie Chartplt.subplot(1, 2, 1)plt.title('Pie Chart', fontweight = 'bold', fontfamily = "sans-serif", color = 'black')plt.pie(df["hotel"].value_counts(), pctdistance = 0.7, autopct = '%.2f%%', labels = labels,wedgeprops = dict(alpha = 0.8, edgecolor = "black"), textprops = {'fontsize': 12}, colors = colors)centre = plt.Circle((0,0), 0.45, fc = "white", edgecolor = "black")plt.gcf().gca().add_artist(centre)# Histogramcountplt = plt.subplot(1, 2, 2)plt.title("Histogram", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "hotel", data = df, order = order, edgecolor = "black", palette = colors)for rect in ax.patches:    ax.text(rect.get_x() + rect.get_width()/2, rect.get_height() + 4.25, rect.get_height(),    horizontalalignment="center", fontsize = 10, bbox = dict(facecolor = "none", edgecolor = "black",    linewidth = 0.25, boxstyle = "round"))plt.xlabel("Hotel", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.xticks([0, 1], labels)plt.grid(axis = "y", alpha = 0.4)df['hotel'].value_counts()

论断：超过 60% 的预订酒店是城市酒店

细分市场剖析

labels = ["Online TA", "Offline TA/TO", "Direct", "Groups", "Corporate", "Complementary", "Aviation"]order = df['market_segment'].value_counts().indexplt.figure(figsize = (22, 9))plt.suptitle('Bookings By Market Segment', fontweight = 'heavy', fontsize = '16',            fontfamily = 'sans-serif', color = "black")# Pie Chartplt.subplot(1, 2, 1)plt.title('Pie Chart', fontweight = 'bold', fontfamily = "sans-serif", color = 'black')plt.pie(df["market_segment"].value_counts(), pctdistance = 0.7, autopct = '%.2f%%', labels = labels,wedgeprops = dict(alpha = 0.8, edgecolor = "black"), textprops = {'fontsize': 12})centre = plt.Circle((0,0), 0.45, fc = "white", edgecolor = "black")plt.gcf().gca().add_artist(centre)# Histogramcountplt = plt.subplot(1, 2, 2)plt.title("Histogram", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "market_segment", data = df, order = order, edgecolor = "black",)for rect in ax.patches:    ax.text(rect.get_x() + rect.get_width()/2, rect.get_height() + 4.25, rect.get_height(),    horizontalalignment="center", fontsize = 10, bbox = dict(facecolor = "none", edgecolor = "black",    linewidth = 0.25, boxstyle = "round"))plt.xlabel("Market Segment", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)df['market_segment'].value_counts()

论断：超过 50% 的预订是通过在线旅行社实现的。

分销渠道剖析

colors = ["#8B7D6B", "#000000", "#CDB79E", "#FFE4C4"]labels = ["TA/TO", "Direct", "Corporate", "GDS"]order = df['distribution_channel'].value_counts().indexplt.figure(figsize = (19, 9))plt.suptitle('Bookings By Distribution Channel', fontweight = 'heavy', fontsize = '16',            fontfamily = 'sans-serif', color = "black")# Pie Chartplt.subplot(1, 2, 1)plt.title('Pie Chart', fontweight = 'bold', fontfamily = "sans-serif", color = 'black')plt.pie(df["distribution_channel"].value_counts(), pctdistance = 0.7, autopct = '%.2f%%', labels = labels, colors = colors,wedgeprops = dict(alpha = 0.8, edgecolor = "black"), textprops = {'fontsize': 12})centre = plt.Circle((0,0), 0.45, fc = "white", edgecolor = "black")plt.gcf().gca().add_artist(centre)# Histogramcountplt = plt.subplot(1, 2, 2)plt.title("Histogram", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "distribution_channel", data = df, order = order, edgecolor = "black", palette = colors)for rect in ax.patches:    ax.text(rect.get_x() + rect.get_width()/2, rect.get_height() + 4.25, rect.get_height(),    horizontalalignment="center", fontsize = 10, bbox = dict(facecolor = "none", edgecolor = "black",    linewidth = 0.25, boxstyle = "round"))plt.xlabel("Distribution Channel", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)df['distribution_channel'].value_counts()

论断：超过 80% 的预订是通过旅行社/运营商实现的。

餐食剖析

colors = ["#6495ED", "#1874CD", "#009ACD", "#00688B"]labels = ["Breakfast", "No Meal", "Half Board", "Full Board"]order = df['meal'].value_counts().indexplt.figure(figsize = (19, 9))plt.suptitle('Bookings By Meals', fontweight = 'heavy', fontsize = '16',            fontfamily = 'sans-serif', color = "black")# Pie Chartplt.subplot(1, 2, 1)plt.title('Pie Chart', fontweight = 'bold', fontfamily = "sans-serif", color = 'black')plt.pie(df["meal"].value_counts(), pctdistance = 0.7, autopct = '%.2f%%', labels = labels, colors = colors,wedgeprops = dict(alpha = 0.8, edgecolor = "black"), textprops = {'fontsize': 12})centre = plt.Circle((0,0), 0.45, fc = "white", edgecolor = "black")plt.gcf().gca().add_artist(centre)# Histogramcountplt = plt.subplot(1, 2, 2)plt.title("Histogram", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "meal", data = df, order = order, edgecolor = "black", palette = colors)for rect in ax.patches:    ax.text(rect.get_x() + rect.get_width()/2, rect.get_height() + 4.25, rect.get_height(),    horizontalalignment="center", fontsize = 10, bbox = dict(facecolor = "none", edgecolor = "black",    linewidth = 0.25, boxstyle = "round"))plt.xlabel("Meal", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)df['meal'].value_counts()

论断：超过 70% 的客人预订早餐，近 90% 的客人预订餐点。

顾客类型剖析

labels = ["Transient", "Transient-Party", "Contract", "Group"]order = df['customer_type'].value_counts().indexplt.figure(figsize = (19, 9))plt.suptitle('Bookings By Customer Type', fontweight = 'heavy', fontsize = '16',            fontfamily = 'sans-serif', color = "black")# Pie Chartplt.subplot(1, 2, 1)plt.title('Pie Chart', fontweight = 'bold', fontfamily = "sans-serif", color = 'black')plt.pie(df["customer_type"].value_counts(), pctdistance = 0.7, autopct = '%.2f%%', labels = labels,wedgeprops = dict(alpha = 0.8, edgecolor = "black"), textprops = {'fontsize': 12})centre = plt.Circle((0,0), 0.45, fc = "white", edgecolor = "black")plt.gcf().gca().add_artist(centre)# Histogramcountplt = plt.subplot(1, 2, 2)plt.title("Histogram", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "customer_type", data = df, order = order, edgecolor = "black")for rect in ax.patches:    ax.text(rect.get_x() + rect.get_width()/2, rect.get_height() + 4.25, rect.get_height(),    horizontalalignment="center", fontsize = 10, bbox = dict(facecolor = "none", edgecolor = "black",    linewidth = 0.25, boxstyle = "round"))plt.xlabel("Customer Type", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)df['customer_type'].value_counts()

论断：大多数人没有抉择跟团游览。

押金状况剖析

plt.figure(figsize = (19, 12))order = sorted(df["deposit_type"].unique())plt.title("Bookings By Deposit Types", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "deposit_type", data = df, hue = "hotel", edgecolor = "black", palette = "bone", order = order)plt.xlabel("Deposit Type", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)df["deposit_type"].value_counts()

论断：大部分客人没有交押金。

客人类型剖析

labels = ['New Guest', 'Repeated Guest']colors = ["#00008B", "#C1CDCD"]order = df['is_repeated_guest'].value_counts().indexplt.figure(figsize = (19, 9))plt.suptitle('Bookings By Type Of Guest', fontweight = 'heavy', fontsize = '16',            fontfamily = 'sans-serif', color = "black")# Pie Chartplt.subplot(1, 2, 1)plt.title('Pie Chart', fontweight = 'bold', fontfamily = "sans-serif", color = 'black')plt.pie(df["is_repeated_guest"].value_counts(), pctdistance = 0.7, autopct = '%.2f%%', labels = labels,wedgeprops = dict(alpha = 0.8, edgecolor = "black"), textprops = {'fontsize': 12}, colors = colors)centre = plt.Circle((0,0), 0.45, fc = "white", edgecolor = "black")plt.gcf().gca().add_artist(centre)# Histogramcountplt = plt.subplot(1, 2, 2)plt.title("Histogram", fontweight = "bold", fontsize = 14, fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "is_repeated_guest", data = df, order = order, edgecolor = "black", palette = colors)for rect in ax.patches:    ax.text(rect.get_x() + rect.get_width()/2, rect.get_height() + 4.25, rect.get_height(),    horizontalalignment="center", fontsize = 10, bbox = dict(facecolor = "none", edgecolor = "black",    linewidth = 0.25, boxstyle = "round"))plt.xlabel("Type Of Guest", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Total", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.xticks([0, 1], labels)plt.grid(axis = "y", alpha = 0.4)df['is_repeated_guest'].value_counts()

论断：简直所有的客人都是新客人。

预订房间类型剖析

plt.figure(figsize = (19, 12))order = sorted(df["reserved_room_type"].unique())plt.title("Bookings By Reserved Room Types", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "reserved_room_type", data = df, hue = "hotel", edgecolor = "black", palette = "bone", order = order)plt.xlabel("Reserved Room Type", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)df["reserved_room_type"].value_counts()

论断：大多数客人预订了房间A，多数预订了房间D和E，其余的需要很少。

调配的房间类型剖析

plt.figure(figsize = (19, 12))order = sorted(df["assigned_room_type"].unique())plt.title("Bookings By Assigned Room Types", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "assigned_room_type", data = df, hue = "hotel", edgecolor = "black", palette = "bone", order = order)plt.xlabel("Assigned Room Type", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)df["assigned_room_type"].value_counts()

论断：大多数客人被调配到 A 室，多数被调配到 D 和 E 室，其余的很少。

预订状态剖析

plt.figure(figsize = (19, 12))order = sorted(df["reservation_status"].unique())plt.title("Bookings By Reservation Status", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "reservation_status", data = df, hue = "hotel", edgecolor = "black", palette = "ocean", order = order)plt.xlabel("Reservation Status", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)df["reservation_status"].value_counts()

论断：大多数客人注销入住并曾经来到。

总住宿夜数散布

plt.figure(figsize = (19, 9))df2 = df.groupby("total_nights")["total_nights"].count()df2.sort_values(ascending = False)[: 10].plot(kind = 'bar')plt.title("Bookings By Total Nights Stayed By Guests", fontweight = "bold", fontsize = 14, fontfamily = "sans-serif", color = 'black')plt.xticks(rotation = 30)plt.xlabel("Number Of Nights", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)

论断：最受欢迎的酒店住宿工夫是三晚。

酒店&总住宿夜数

plt.figure(figsize = (19, 12))order = df.total_nights.value_counts().iloc[:10].indexplt.title("Total Nights Stayed By Guests In Hotel", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "total_nights", data = df, hue = "hotel", edgecolor = "black", palette = "ocean", order = order)plt.xlabel("Total Nights", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)

论断：度假酒店最受欢迎的住宿工夫是一晚、七晚、两晚、三晚和四晚。城市酒店最受欢迎的住宿工夫是三晚、两晚、一晚和四晚。

预约下单工夫

plt.figure(figsize = (16, 6))plt.title("Bookings By Lead Time", fontweight = "bold", fontsize = 14, fontfamily = 'sans-serif', color = 'black')sns.histplot(data = df, x = 'lead_time', hue = "hotel", kde = True, color = "#104E8B")plt.xlabel('Lead Time', fontweight = 'normal', fontsize = 11, fontfamily = 'sans-serif', color = "black")plt.ylabel('Number Of Bookings', fontweight = 'regular', fontsize = 11, fontfamily = "sans-serif", color = "black")df["lead_time"].describe().T

论断：大多数预订是在入住酒店前 100 天内实现的。

关联剖析

预订勾销&酒店类型

plt.figure(figsize = (19, 12))plt.title("Number Of Bookings Cancelled By Guests", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "hotel", data = df, hue = "is_canceled", edgecolor = "black", palette = "bone")for rect in ax.patches:    ax.text(rect.get_x() + rect.get_width()/2, rect.get_height() + 4.25, rect.get_height(),    horizontalalignment="center", fontsize = 10, bbox = dict(facecolor = "none", edgecolor = "black",    linewidth = 0.25, boxstyle = "round"))plt.xlabel("Hotel", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)

论断：度假村酒店的客人勾销预订的频率低于城市酒店的客人。

预约勾销&新老客

plt.figure(figsize = (19, 12))plt.title("Number Of Bookings Cancelled By Type Of Guests", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "is_canceled", data = df, hue = "is_repeated_guest", edgecolor = "black", palette = "bone")for rect in ax.patches:    ax.text(rect.get_x() + rect.get_width()/2, rect.get_height() + 4.25, rect.get_height(),    horizontalalignment="center", fontsize = 10, bbox = dict(facecolor = "none", edgecolor = "black",    linewidth = 0.25, boxstyle = "round"))plt.xlabel("Cancellation", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.legend(['New Guest', 'Repeated Guest'])plt.grid(axis = "y", alpha = 0.4)

论断：老客勾销预订的次数少于新客。

预约勾销&细分市场

plt.figure(figsize = (19, 12))plt.title("Number Of Bookings Cancelled By Market Segments", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "market_segment", data = df, hue = "is_canceled", edgecolor = "black", palette = "bone")for rect in ax.patches:    ax.text(rect.get_x() + rect.get_width()/2, rect.get_height() + 4.25, rect.get_height(),    horizontalalignment="center", fontsize = 10, bbox = dict(facecolor = "none", edgecolor = "black",    linewidth = 0.25, boxstyle = "round"))plt.xlabel("Market Segment", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)

论断：在线旅行社、线下旅行社/运营商和直销局部的勾销率高于其余局部。

预订数量&年份

plt.figure(figsize = (19, 12))plt.title("Number Of Bookings Per Year", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "arrival_date_year", data = df, hue = "hotel", edgecolor = "black", palette = "cool")for rect in ax.patches:    ax.text(rect.get_x() + rect.get_width()/2, rect.get_height() + 4.25, rect.get_height(),    horizontalalignment="center", fontsize = 10, bbox = dict(facecolor = "none", edgecolor = "black",    linewidth = 0.25, boxstyle = "round"))plt.xlabel("Year", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)df["arrival_date_year"].value_counts()

论断：度假村和城市酒店在 2016 年的预订量均最高。与度假村酒店相比，城市酒店在 2017 年的预订量更高。两者在 2015 年的预订量简直雷同。

预订数量&月份

plt.figure(figsize = (16, 10))plt.title("Number Of Bookings Per Customer Type", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "customer_type", data = df, hue = "hotel", edgecolor = "black", palette = "pink")for rect in ax.patches:    ax.text(rect.get_x() + rect.get_width()/2, rect.get_height() + 4.25, rect.get_height(),    horizontalalignment="center", fontsize = 10, bbox = dict(facecolor = "none", edgecolor = "black",    linewidth = 0.25, boxstyle = "round"))plt.xlabel("Customer Type", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)

论断：十一月、十二月、一月和二月是预约起码的月份，7 月和 8 月是预订顶峰月份。

预订数量&客户类型

months = ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October","November", "December"]plt.figure(figsize = (19, 12))plt.title("Number Of Bookings Per Month", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')d = df.groupby("arrival_date_month")["arrival_date_month"].count()sns.barplot(x = d.index, y = d, order = months)plt.xticks(rotation = 30)plt.xlabel("Months")plt.ylabel("Number Of Bookings")df["arrival_date_month"].value_counts()

论断：长期和长期派对客人大多预订城市酒店，而跟团客人在度假村和城市酒店的预订数量简直雷同。

车位&预订

plt.figure(figsize = (19, 12))plt.title("Number Of Bookings Per Required Car Parking Space", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')ax = sns.countplot(x = "required_car_parking_spaces", data = df, hue = "hotel", edgecolor = "black", palette = "cool")plt.xlabel("Required Car Parking Space", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.ylabel("Number Of Bookings", fontweight = "bold", fontsize = 11, fontfamily = "sans-serif", color = "black")plt.grid(axis = "y", alpha = 0.4)df["required_car_parking_spaces"].value_counts()

论断：大多数客人不须要停车位，而多数客人须要停车位。

国家/地区&特殊要求数量

df2 = df.groupby("country")["total_of_special_requests"].mean().sort_values(ascending = False)[: 20]plt.figure(figsize = (18, 8))sns.barplot(x = df2.index, y = df2)plt.xticks(rotation = 30)plt.xlabel("Country")plt.ylabel("Average Number Of Special Requests")plt.title("Average Number Of Special Requests Made By Top 20 Countries ", fontweight = "bold", fontsize = 14,fontfamily = "sans-serif", color = 'black')

论断：在这些国家中，博茨瓦纳的特殊要求数量最多。

客户类型&特殊要求数量

df2 = df.groupby("customer_type")["total_of_special_requests"].mean().sort_values(ascending = False)[: 20]plt.figure(figsize = (18, 8))sns.barplot(x = df2.index, y = df2)plt.xticks(rotation = 30)plt.xlabel("Customer Type")plt.ylabel("Average Number Of Special Requests")plt.title("Average Number Of Special Requests By Customer Type", fontweight = "bold", fontsize = 14, fontfamily = "sans-serif", color = 'black')

论断：跟团客人的特殊要求数量最多，而长期派对客人的特殊要求数量起码。

月份&特殊要求数量

months = ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October","November", "December"]df2 = df.groupby("arrival_date_month")["total_of_special_requests"].mean().sort_values(ascending = False)[: 20]plt.figure(figsize = (18, 8))sns.barplot(x = df2.index, y = df2, order = months)plt.xticks(rotation = 30)plt.xlabel("Months")plt.ylabel("Average Number Of Special Requests")plt.title("Average Number Of Special Requests By Guests Per Months ", fontweight = "bold", fontsize = 14, fontfamily = "sans-serif", color = 'black')

论断：客人在几个月内提出了简直类似数量的特殊要求，但在 8 月、7 月和 12 月提出的特殊要求略多一些。

酒店类型&价格

# Histogramfig = plt.figure(figsize = (16, 10))df.drop(df[df["adr"] == 5400].index, axis = 0, inplace = True)plt.suptitle("Average Daily Rate Per Hotel", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')plot1 = fig.add_subplot(1, 2, 2)plt.title("Histogram Plot", fontweight = "bold", fontsize = 14, fontfamily = 'sans-serif', color = 'black')sns.histplot(data = df, x = 'adr', hue = "hotel", kde = True, color = "#104E8B")plt.xlabel('Average Daily Rate', fontweight = 'normal', fontsize = 11, fontfamily = 'sans-serif', color = "black")plt.ylabel('Count', fontweight = 'regular', fontsize = 11, fontfamily = "sans-serif", color = "black")# Box Plotplot2 = fig.add_subplot(1, 2, 1)plt.title("Box Plot", fontweight = "bold", fontsize = 14, fontfamily = 'sans-serif', color = 'black')sns.boxplot(data = df, x = "hotel", y = 'adr', color = "#104E8B")plt.ylabel('Average Daily Rate', fontweight = 'regular', fontsize = 11, fontfamily = 'sans-serif', color = "black")plt.show()df["adr"].describe()

论断：度假村酒店的均匀每日价格比城市酒店更扩散。

月份&费率

months = ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October","November", "December"]df.drop(df[df["adr"] == 5400].index, axis = 0, inplace = True)d = df.groupby(["hotel", "arrival_date_month"])["adr"].mean().reset_index()d["arrival_date_month"] = pd.Categorical(d["arrival_date_month"], categories = months, ordered = True)d.sort_values("arrival_date_month", inplace = True)fig = plt.figure(figsize = (16, 10))plt.suptitle("Average Daily Rate Per Month", fontweight = "bold", fontsize = 14,         fontfamily = "sans-serif", color = 'black')sns.lineplot(data = d, y = 'adr', x = "arrival_date_month", hue = "hotel")plt.ylabel('Average Daily Rate', fontweight = 'normal', fontsize = 11, fontfamily = 'sans-serif', color = "black")plt.xlabel('Months', fontweight = 'regular', fontsize = 11, fontfamily = "sans-serif", color = "black")plt.xticks(rotation = 30)

论断：两类酒店的均匀每日房价在年中均较高。与度假村酒店相比，城市酒店在年初和年末的每日房价较高。