主頁 >  其他 > 業務資料分析最佳案例！旅游業資料分析！?

業務資料分析最佳案例！旅游業資料分析！?

2022-11-28 06:57:05 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
?? 宣告：著作權所有，轉載請聯系平臺與作者并注明出處
?? 收藏ShowMeAI查看更多精彩內容

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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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/p/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

轉載請註明出處，本文鏈接：https://www.uj5u.com/qita/538543.html

標籤：其他

上一篇：HCIE Routing&Switching之MPLS基礎理論

下一篇：MYSQL事務

標籤雲
其他(157675) Python(38076) JavaScript(25376) Java(17977) C(15215) 區塊鏈(8255) C＃(7972) AI(7469) 爪哇(7425) MySQL(7132) html(6777) 基礎類(6313) sql(6102) 熊猫(6058) PHP(5869) 数组(5741) R(5409) Linux(5327) 反应(5209) 腳本語言(PerlPython)(5129) 非技術區(4971) Android(4554) 数据框(4311) css(4259) 节点.js(4032) C語言(3288) json(3245) 列表(3129) 扑(3119) C++語言(3117) 安卓(2998) 打字稿(2995) VBA(2789) Java相關(2746) 疑難問題(2699) 细绳(2522) 單片機工控(2479) iOS(2429) ASP.NET(2402) MongoDB(2323) 麻木的(2285) 正则表达式(2254) 字典(2211) 循环(2198) 迅速(2185) 擅长(2169) 镖(2155) 功能(1967) .NET技术(1958) Web開發(1951) python-3.x(1918) HtmlCss(1915) 弹簧靴(1913) C++(1909) xml(1889) PostgreSQL(1872) .NETCore(1853) 谷歌表格(1846) Unity3D(1843) for循环(1842)

熱門瀏覽
  • 網閘典型架構簡述

    網閘架構一般分為兩種：三主機的三系統架構網閘和雙主機的2+1架構網閘。 三主機架構分別為內端機、外端機和仲裁機。三機無論從軟體和硬體上均各自獨立。首先從硬體上來看，三機都用各自獨立的主板、記憶體及存盤設備。從軟體上來看，三機有各自獨立的作業系統。這樣能達到完全的三機獨立。對于“2+1”系統，“2”分為 ......

    uj5u.com 2020-09-10 02:00:44 more
  • 如何從xshell上傳檔案到centos linux虛擬機里

    如何從xshell上傳檔案到centos linux虛擬機里及：虛擬機CentOs下執行 yum -y install lrzsz命令，出現錯誤：鏡像無法找到軟體包 前言 一、安裝lrzsz步驟 二、上傳檔案 三、遇到的問題及解決方案 總結 前言 提示：其實很簡單，往虛擬機上安裝一個上傳檔案的工具 ......

    uj5u.com 2020-09-10 02:00:47 more
  • 一、SQLMAP入門

    一、SQLMAP入門 1、判斷是否存在注入 sqlmap.py -u 網址/id=1 id=1不可缺少。當注入點后面的引數大于兩個時。需要加雙引號， sqlmap.py -u "網址/id=1&uid=1" 2、判斷文本中的請求是否存在注入 從文本中加載http請求，SQLMAP可以從一個文本檔案中 ......

    uj5u.com 2020-09-10 02:00:50 more
  • Metasploit 簡單使用教程

    metasploit 簡單使用教程 浩先生， 2020-08-28 16:18:25 分類專欄： kail 網路安全 linux 文章標簽： linux資訊安全 編輯 著作權 metasploit 使用教程 前言 一、Metasploit是什么？ 二、準備作業 三、具體步驟 前言 Msfconsole ......

    uj5u.com 2020-09-10 02:00:53 more
  • 游戲逆向之驅動層與用戶層通訊

    驅動層代碼： #pragma once #include <ntifs.h> #define add_code CTL_CODE(FILE_DEVICE_UNKNOWN,0x800,METHOD_BUFFERED,FILE_ANY_ACCESS) /* 更多游戲逆向視頻www.yxfzedu.com ......

    uj5u.com 2020-09-10 02:00:56 more
  • 北斗電力時鐘（北斗授時服務器）讓網路資料更精準

    北斗電力時鐘（北斗授時服務器）讓網路資料更精準 北斗電力時鐘（北斗授時服務器）讓網路資料更精準 京準電子科技官微——ahjzsz 近幾年，資訊技術的得了快速發展，互聯網在逐漸普及，其在人們生活和生產中都得到了廣泛應用，并且取得了不錯的應用效果。計算機網路資訊在電力系統中的應用，一方面使電力系統的運行 ......

    uj5u.com 2020-09-10 02:01:03 more
  • 【CTF】CTFHub 技能樹 彩蛋 writeup

    ?碎碎念 CTFHub：https://www.ctfhub.com/ 筆者入門CTF時時剛開始刷的是bugku的舊平臺，后來才有了CTFHub。 感覺不論是網頁UI設計，還是題目質量，賽事跟蹤，工具軟體都做得很不錯。 而且因為獨到的金幣制度的確讓人有一種想去刷題賺金幣的感覺。 個人還是非常喜歡這個 ......

    uj5u.com 2020-09-10 02:04:05 more
  • 02windows基礎操作

    我學到了一下幾點 Windows系統目錄結構與滲透的作用 常見Windows的服務詳解 Windows埠詳解 常用的Windows注冊表詳解 hacker DOS命令詳解（net user / type /md /rd/ dir /cd /net use copy、批處理 等） 利用dos命令制作 ......

    uj5u.com 2020-09-10 02:04:18 more
  • 03.Linux基礎操作

    我學到了以下幾點 01Linux系統介紹02系統安裝，密碼啊破解03Linux常用命令04LAMP 01LINUX windows： win03 8 12 16 19 配置不繁瑣 Linux：redhat,centos(紅帽社區版)，Ubuntu server,suse unix:金融機構，證券，銀 ......

    uj5u.com 2020-09-10 02:04:30 more
  • 05HTML

    01HTML介紹 02頭部標簽講解03基礎標簽講解04表單標簽講解 HTML前段語言 js1.了解代碼2.根據代碼 懂得挖掘漏洞 （POST注入/XSS漏洞上傳）3.黑帽seo 白帽seo 客戶網站被黑帽植入劫持代碼如何處理4.熟悉html表單 <html><head><title>TDK標題，描述 ......

    uj5u.com 2020-09-10 02:04:36 more
最新发布
  • 2023年最新微信小程式抓包教程

    01 開門見山 隔一個月發一篇文章，不過分。 首先回顧一下《微信系結手機號資料庫被脫庫事件》，我也是第一時間得知了這個訊息，然后跟蹤了整件事情的經過。下面是這起事件的相關截圖以及近日流出的一萬條資料樣本： 個人認為這件事也沒什么，還不如關注一下之前45億快遞資料查詢渠道疑似在近日復活的訊息。 訊息是 ......

    uj5u.com 2023-04-20 08:48:24 more
  • web3 產品介紹：metamask 錢包 使用最多的瀏覽器插件錢包

    Metamask錢包是一種基于區塊鏈技術的數字貨幣錢包，它允許用戶在安全、便捷的環境下管理自己的加密資產。Metamask錢包是以太坊生態系統中最流行的錢包之一，它具有易于使用、安全性高和功能強大等優點。 本文將詳細介紹Metamask錢包的功能和使用方法。 一、 Metamask錢包的功能 數字資 ......

    uj5u.com 2023-04-20 08:47:46 more
  • vulnhub_Earth

    前言 靶機地址->>>vulnhub_Earth 攻擊機ip：192.168.20.121 靶機ip：192.168.20.122 參考文章 https://www.cnblogs.com/Jing-X/archive/2022/04/03/16097695.html https://www.cnb ......

    uj5u.com 2023-04-20 07:46:20 more
  • 從4k到42k，軟體測驗工程師的漲薪史，給我看哭了

    清明節一過，盲猜大家已經無心上班，在數著日子準備過五一，但一想到銀行卡里的余額……瞬間心情就不美麗了。最近，2023年高校畢業生就業調查顯示，本科畢業月平均起薪為5825元。調查一出，便有很多同學表示自己又被平均了。看著這一資料，不免讓人想到前不久中國青年報的一項調查：近六成大學生認為畢業10年內會 ......

    uj5u.com 2023-04-20 07:44:00 more
  • 最新版本 Stable Diffusion 開源 AI 繪畫工具之中文自動提詞篇

    🎈 標簽生成器 由于輸入正向提示詞 prompt 和反向提示詞 negative prompt 都是使用英文，所以對學習母語的我們非常不友好 使用網址：https://tinygeeker.github.io/p/ai-prompt-generator 這個網址是為了讓大家在使用 AI 繪畫的時候 ......

    uj5u.com 2023-04-20 07:43:36 more
  • 漫談前端自動化測驗演進之路及測驗工具分析

    隨著前端技術的不斷發展和應用程式的日益復雜，前端自動化測驗也在不斷演進。隨著 Web 應用程式變得越來越復雜，自動化測驗的需求也越來越高。如今，自動化測驗已經成為 Web 應用程式開發程序中不可或缺的一部分，它們可以幫助開發人員更快地發現和修復錯誤，提高應用程式的性能和可靠性。 ......

    uj5u.com 2023-04-20 07:43:16 more
  • CANN開發實踐：4個DVPP記憶體問題的典型案例解讀

    摘要：由于DVPP媒體資料處理功能對存放輸入、輸出資料的記憶體有更高的要求（例如，記憶體首地址128位元組對齊），因此需呼叫專用的記憶體申請介面，那么本期就分享幾個關于DVPP記憶體問題的典型案例，并給出原因分析及解決方法。 本文分享自華為云社區《FAQ_DVPP記憶體問題案例》，作者：昇騰CANN。 DVPP ......

    uj5u.com 2023-04-20 07:43:03 more
  • msf學習

    msf學習 以kali自帶的msf為例 一、msf核心模塊與功能 msf模塊都放在/usr/share/metasploit-framework/modules目錄下 1、auxiliary 輔助模塊，輔助滲透（埠掃描、登錄密碼爆破、漏洞驗證等） 2、encoders 編碼器模塊，主要包含各種編碼 ......

    uj5u.com 2023-04-20 07:42:59 more
  • Halcon軟體安裝與界面簡介

    1. 下載Halcon17版本到到本地 2. 雙擊安裝包后 3. 步驟如下 1.2 Halcon軟體安裝 界面分為四大塊 1. Halcon的五個助手 1) 影像采集助手：與相機連接，設定相機引數，采集影像 2) 標定助手：九點標定或是其它的標定，生成標定檔案及內參外參，可以將像素單位轉換為長度單位 ......

    uj5u.com 2023-04-20 07:42:17 more
  • 在MacOS下使用Unity3D開發游戲

    第一次發博客，先發一下我的游戲開發環境吧。 去年2月份買了一臺MacBookPro2021 M1pro(以下簡稱mbp)，這一年來一直在用mbp開發游戲。我大致分享一下我的開發工具以及使用體驗。 1、Unity 官網鏈接： https://unity.cn/releases 我一般使用的Apple ......

    uj5u.com 2023-04-20 07:40:19 more

Copyright © 2010-2020 有解無憂