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業務資料分析最佳案例！旅游業資料分析！?

2022-11-28 07:24:00 其他

?? 作者：韓信子@ShowMeAI
?? 資料分析實戰系列：https://www.showmeai.tech/tutorials/40
?? 本文地址：https://www.showmeai.tech/article-detail/388
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在本篇內容中，ShowMeAI將帶大家對旅游業，主要是酒店預訂需求進行分析，我們使用到的資料集包含城市酒店和度假酒店的預訂資訊，包括預訂時間、住宿時長、客人入住的周末或作業日晚數以及可用停車位數量等資訊，

我們本次用到的是 ??酒店預訂資料集，包含 119390 位客人，有 32 個特征欄位，大家可以通過 ShowMeAI 的百度網盤地址下載，

?? 實戰資料集下載（百度網盤）：公?眾?號『ShowMeAI研究中心』回復『實戰』，或者點擊 這里 獲取本文 [59]旅游業大資料多維度業務分析案例 『酒店預訂資料集』

? ShowMeAI官方GitHub：https://github.com/ShowMeAI-Hub

本文資料分析部分涉及的工具庫，大家可以參考ShowMeAI制作的工具庫速查表和教程進行學習和快速使用，

?? 資料科學工具庫速查表 | Pandas 速查表
?? 圖解資料分析：從入門到精通系列教程

?? 匯入工具庫

# 資料處理&科學計算
import pandas as pd
import numpy as np

# 資料分析&繪圖
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff

import warnings
warnings.filterwarnings("ignore")

# 科學計算
from scipy.stats import skew,kurtosis
import pylab as py

# 時間
import time
import datetime
from datetime import datetime
from datetime import date

?? 讀取資料

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

?? 資料資訊初覽

df.info()

?? 資料預處理

?? 清洗&缺失值處理

首先統計欄位缺失值比例

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

我們對有缺失的欄位做一些缺失值填充作業

# 填充"agent" 和 "company" 欄位中的缺失值
df["agent"].fillna(0, inplace = True)
df["company"].fillna(0, inplace = True)

# 使用眾數填充"country"欄位缺失值
df["country"].fillna(df["country"].mode()[0], inplace = True)

# 洗掉包含"children"缺失值的資料記錄
df.dropna(subset = ["children"], axis = 0, inplace = True)

?? 欄位資料處理

# 將“distribution_channel”列中的“Undefined”轉換為“TA/TO”
df["distribution_channel"].replace("Undefined", "TA/TO", inplace = True)

# meal欄位映射處理
df["meal"].replace(["Undefined", "BB", "FB", "HB", "SC" ], ["No Meal", "Breakfast", "Full Board", "Half Board", "No Meal"], inplace = True)

# 將“is_canceled”列的值從 0 和 1 轉換為“Cancelled”和“Not Cancelled”
df["is_canceled"].replace([0, 1], ["Cancelled", "Not Cancelled"], inplace = True)

?? 調整資料型別

  • 將children、agent和company列的資料型別轉換為整型
  • 將reservation_status_date列的資料型別從物件轉換為日期型別
# 轉整型
df["children"].astype(int)
df["agent"].astype(int)
df["company"].astype(int)

# 時間型
pd.to_datetime(df["reservation_status_date"])

?? 重復資料處理

df.drop_duplicates(inplace = True)

?? 構建匯總欄位

我們對顧客總體的居住晚數進行統計

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

?? 描述性統計

我們基于pandas的簡單功能，對資料的統計分布做一個處理了解

df.describe().T

?? 探索性資料分析

?? 酒店維度分析

# 我們對 城市酒店 和 度假酒店 進行統計分析
labels = ['City Hotel', 'Resort Hotel']
colors = ["#538B8B", "#7AC5CD"]
order = df['hotel'].value_counts().index
plt.figure(figsize = (19, 9))
plt.suptitle('Bookings By Hotels', fontweight = 'heavy', fontsize = '16',
            fontfamily = 'sans-serif', color = "black")
# Pie Chart
plt.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)
# Histogram
countplt = plt.subplot(1, 2, 2)
plt.title("Histogram", fontweight = "bold", fontsize = 14, 
        fontfamily = "sans-serif", color = 'black')
ax = sns.countplot(x = "hotel", data = https://www.cnblogs.com/showmeai/archive/2022/11/27/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()

?? 結論：超過 60% 的預訂酒店是城市酒店

?? 細分市場分析

labels = ["Online TA", "Offline TA/TO", "Direct", "Groups", "Corporate", "Complementary", "Aviation"]
order = df['market_segment'].value_counts().index
plt.figure(figsize = (22, 9))
plt.suptitle('Bookings By Market Segment', fontweight = 'heavy', fontsize = '16',
            fontfamily = 'sans-serif', color = "black")
# Pie Chart
plt.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)
# Histogram
countplt = plt.subplot(1, 2, 2)
plt.title("Histogram", fontweight = "bold", fontsize = 14, 
        fontfamily = "sans-serif", color = 'black')
ax = sns.countplot(x = "market_segment", data = https://www.cnblogs.com/showmeai/archive/2022/11/27/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()

?? 結論：超過 50% 的預訂是通過在線旅行社完成的，

?? 分銷渠道分析

colors = ["#8B7D6B", "#000000", "#CDB79E", "#FFE4C4"]
labels = ["TA/TO", "Direct", "Corporate", "GDS"]
order = df['distribution_channel'].value_counts().index
plt.figure(figsize = (19, 9))
plt.suptitle('Bookings By Distribution Channel', fontweight = 'heavy', fontsize = '16',
            fontfamily = 'sans-serif', color = "black")
# Pie Chart
plt.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)
# Histogram
countplt = plt.subplot(1, 2, 2)
plt.title("Histogram", fontweight = "bold", fontsize = 14, 
        fontfamily = "sans-serif", color = 'black')
ax = sns.countplot(x = "distribution_channel", data = https://www.cnblogs.com/showmeai/archive/2022/11/27/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()

?? 結論：超過 80% 的預訂是通過旅行社/運營商完成的，

?? 餐食分析

colors = ["#6495ED", "#1874CD", "#009ACD", "#00688B"]
labels = ["Breakfast", "No Meal", "Half Board", "Full Board"]
order = df['meal'].value_counts().index
plt.figure(figsize = (19, 9))
plt.suptitle('Bookings By Meals', fontweight = 'heavy', fontsize = '16',
            fontfamily = 'sans-serif', color = "black")
# Pie Chart
plt.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)
# Histogram
countplt = plt.subplot(1, 2, 2)
plt.title("Histogram", fontweight = "bold", fontsize = 14, 
        fontfamily = "sans-serif", color = 'black')
ax = sns.countplot(x = "meal", data = https://www.cnblogs.com/showmeai/archive/2022/11/27/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()

?? 結論：超過 70% 的客人預訂早餐，近 90% 的客人預訂餐點，

?? 顧客型別分析

labels = ["Transient", "Transient-Party", "Contract", "Group"]
order = df['customer_type'].value_counts().index
plt.figure(figsize = (19, 9))
plt.suptitle('Bookings By Customer Type', fontweight = 'heavy', fontsize = '16',
            fontfamily = 'sans-serif', color = "black")
# Pie Chart
plt.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)
# Histogram
countplt = plt.subplot(1, 2, 2)
plt.title("Histogram", fontweight = "bold", fontsize = 14, 
        fontfamily = "sans-serif", color = 'black')
ax = sns.countplot(x = "customer_type", data = https://www.cnblogs.com/showmeai/archive/2022/11/27/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()

?? 結論：大多數人沒有選擇跟團旅游，

?? 押金情況分析

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 = https://www.cnblogs.com/showmeai/archive/2022/11/27/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()

?? 結論：大部分客人沒有交押金，

?? 客人型別分析

labels = ['New Guest', 'Repeated Guest']
colors = ["#00008B", "#C1CDCD"]
order = df['is_repeated_guest'].value_counts().index
plt.figure(figsize = (19, 9))
plt.suptitle('Bookings By Type Of Guest', fontweight = 'heavy', fontsize = '16',
            fontfamily = 'sans-serif', color = "black")
# Pie Chart
plt.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)
# Histogram
countplt = 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 = https://www.cnblogs.com/showmeai/archive/2022/11/27/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()

?? 結論：幾乎所有的客人都是新客人，

?? 預訂房間型別分析

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 = https://www.cnblogs.com/showmeai/archive/2022/11/27/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()

?? 結論：大多數客人預訂了房間A，少數預訂了房間D和E，其余的需求很少，

?? 分配的房間型別分析

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 = https://www.cnblogs.com/showmeai/archive/2022/11/27/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()

?? 結論：大多數客人被分配到 A 室，少數被分配到 D 和 E 室，其余的很少，

?? 預訂狀態分析

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 = https://www.cnblogs.com/showmeai/archive/2022/11/27/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()

?? 結論：大多數客人登記入住并已經離開，

?? 總住宿夜數分布

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)

?? 結論：最受歡迎的酒店住宿時間是三晚，

?? 酒店&總住宿夜數

plt.figure(figsize = (19, 12))
order = df.total_nights.value_counts().iloc[:10].index
plt.title("Total Nights Stayed By Guests In Hotel", fontweight = "bold", fontsize = 14, 
        fontfamily = "sans-serif", color = 'black')
ax = sns.countplot(x = "total_nights", data = https://www.cnblogs.com/showmeai/archive/2022/11/27/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)

?? 結論：度假酒店最受歡迎的住宿時間是一晚、七晚、兩晚、三晚和四晚，城市酒店最受歡迎的住宿時間是三晚、兩晚、一晚和四晚，

?? 熱門國家分布

plt.figure(figsize = (19, 9))
df2 = df.groupby("country")["country"].count()
df2.sort_values(ascending = False)[: 20].plot(kind = 'bar')
plt.title("Bookings By Top 20 Countries", fontweight = "bold", fontsize = 14, fontfamily = "sans-serif", color = 'black')
plt.xticks(rotation = 30)
plt.xlabel("Country", 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["country"].value_counts()

?? 結論：在這份資料中，葡萄牙的預訂量比其他任何國家都多，

?? 預定下單時間

plt.figure(figsize = (16, 6))
plt.title("Bookings By Lead Time", fontweight = "bold", fontsize = 14, fontfamily = 'sans-serif', color = 'black')
sns.histplot(data = https://www.cnblogs.com/showmeai/archive/2022/11/27/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

?? 結論：大多數預訂是在入住酒店前 100 天內完成的，

?? 關聯分析

?? 預訂取消&酒店型別

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 = https://www.cnblogs.com/showmeai/archive/2022/11/27/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)

?? 結論：度假村酒店的客人取消預訂的頻率低于城市酒店的客人，

?? 預約取消&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 = https://www.cnblogs.com/showmeai/archive/2022/11/27/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)

?? 結論：老客取消預訂的次數少于新客，

?? 預約取消&細分市場

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 = https://www.cnblogs.com/showmeai/archive/2022/11/27/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)

?? 結論：在線旅行社、線下旅行社/運營商和直銷部分的取消率高于其他部分，

?? 預訂數量&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 = https://www.cnblogs.com/showmeai/archive/2022/11/27/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()

?? 結論：度假村和城市酒店在 2016 年的預訂量均最高，與度假村酒店相比，城市酒店在 2017 年的預訂量更高，兩者在 2015 年的預訂量幾乎相同，

?? 預訂數量&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 = https://www.cnblogs.com/showmeai/archive/2022/11/27/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)

?? 結論：十一月、十二月、一月和二月是預定最少的月份，7 月和 8 月是預訂高峰月份，

?? 預訂數量&客戶型別

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()

?? 結論：臨時和臨時派對客人大多預訂城市酒店，而跟團客人在度假村和城市酒店的預訂數量幾乎相同，

?? 車位&預訂

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 = https://www.cnblogs.com/showmeai/archive/2022/11/27/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()

?? 結論：大多數客人不需要停車位，而少數客人需要停車位，

?? 國家/地區&特殊要求數量

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')

?? 結論：在這些國家中，博茨瓦納的特殊要求數量最多，

?? 客戶型別&特殊要求數量

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')

?? 結論：跟團客人的特殊要求數量最多，而臨時派對客人的特殊要求數量最少，

?? 月份&特殊要求數量

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')

?? 結論：客人在幾個月內提出了幾乎相似數量的特殊要求，但在 8 月、7 月和 12 月提出的特殊要求略多一些，

?? 酒店型別&價格

# Histogram
fig = 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 = https://www.cnblogs.com/showmeai/archive/2022/11/27/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 Plot
plot2 = fig.add_subplot(1, 2, 1)
plt.title("Box Plot", fontweight = "bold", fontsize = 14, fontfamily = 'sans-serif', color = 'black')
sns.boxplot(data = https://www.cnblogs.com/showmeai/archive/2022/11/27/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()

?? 結論：度假村酒店的平均每日價格比城市酒店更分散，

?? 月份&費率

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 = https://www.cnblogs.com/showmeai/archive/2022/11/27/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)

?? 結論：兩類酒店的平均每日房價在年中均較高，與度假村酒店相比，城市酒店在年初和年末的每日房價較高，

?? 相關性分析

?? 相關矩陣

我們計算一下相關矩陣，看看欄位間的相關性如何

# 剔除一些不參與相關分析的欄位
df_sub = df.drop(['arrival_date_week_number', 'arrival_date_day_of_month', 'previous_cancellations','previous_bookings_not_canceled', 'booking_changes', 'reservation_status_date', 'agent', 'company', 'days_in_waiting_list', 'adults', 'babies', 'children'], axis = 1)

# 相關矩陣
corr_matrix = round(df_sub.corr(), 3)
"Correlation Matrix: "
corr_matrix

?? 熱力圖

我們做一個熱力圖的繪制，以便更清晰看到欄位間相關性，

plt.rcParams['figure.figsize'] =(12, 6)
sns.heatmap(df_sub.corr(), annot=True, cmap='Reds', linewidths=5)
plt.suptitle('Correlation Between Variables', fontweight='heavy', x=0.03, y=0.98, ha = "left", fontsize='18', fontfamily='sans-serif', color= "black")

參考資料

  • ?? 資料科學工具庫速查表 | Pandas 速查表：https://www.showmeai.tech/article-detail/101
  • ?? 圖解資料分析：從入門到精通系列教程：https://www.showmeai.tech/tutorials/33

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