kaggle数分项目 | netfix影视内容分析
2024-06-08 06:59:34
数据集来源：
https://www.kaggle.com/shivamb/netflix-shows
参考：
https://www.kaggle.com/code/shivamb/netflix-shows-and-movies-exploratory-analysis/notebook
import plotly.graph_objects as go
from plotly.offline import init_notebook_mode, iplot
import pandas as pd df = pd.read_csv("./netflix_titles.csv")
df.head(10)
| show_id | type | title | director | cast | country | date_added | release_year | rating | duration | listed_in | description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | s1 | Movie | Dick Johnson Is Dead | Kirsten Johnson | NaN | United States | September 25, 2021 | 2020 | PG-13 | 90 min | Documentaries | As her father nears the end of his life, filmm... |
| 1 | s2 | TV Show | Blood & Water | NaN | Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... | South Africa | September 24, 2021 | 2021 | TV-MA | 2 Seasons | International TV Shows, TV Dramas, TV Mysteries | After crossing paths at a party, a Cape Town t... |
| 2 | s3 | TV Show | Ganglands | Julien Leclercq | Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi... | NaN | September 24, 2021 | 2021 | TV-MA | 1 Season | Crime TV Shows, International TV Shows, TV Act... | To protect his family from a powerful drug lor... |
| 3 | s4 | TV Show | Jailbirds New Orleans | NaN | NaN | NaN | September 24, 2021 | 2021 | TV-MA | 1 Season | Docuseries, Reality TV | Feuds, flirtations and toilet talk go down amo... |
| 4 | s5 | TV Show | Kota Factory | NaN | Mayur More, Jitendra Kumar, Ranjan Raj, Alam K... | India | September 24, 2021 | 2021 | TV-MA | 2 Seasons | International TV Shows, Romantic TV Shows, TV ... | In a city of coaching centers known to train I... |
| 5 | s6 | TV Show | Midnight Mass | Mike Flanagan | Kate Siegel, Zach Gilford, Hamish Linklater, H... | NaN | September 24, 2021 | 2021 | TV-MA | 1 Season | TV Dramas, TV Horror, TV Mysteries | The arrival of a charismatic young priest brin... |
| 6 | s7 | Movie | My Little Pony: A New Generation | Robert Cullen, José Luis Ucha | Vanessa Hudgens, Kimiko Glenn, James Marsden, ... | NaN | September 24, 2021 | 2021 | PG | 91 min | Children & Family Movies | Equestria's divided. But a bright-eyed hero be... |
| 7 | s8 | Movie | Sankofa | Haile Gerima | Kofi Ghanaba, Oyafunmike Ogunlano, Alexandra D... | United States, Ghana, Burkina Faso, United Kin... | September 24, 2021 | 1993 | TV-MA | 125 min | Dramas, Independent Movies, International Movies | On a photo shoot in Ghana, an American model s... |
| 8 | s9 | TV Show | The Great British Baking Show | Andy Devonshire | Mel Giedroyc, Sue Perkins, Mary Berry, Paul Ho... | United Kingdom | September 24, 2021 | 2021 | TV-14 | 9 Seasons | British TV Shows, Reality TV | A talented batch of amateur bakers face off in... |
| 9 | s10 | Movie | The Starling | Theodore Melfi | Melissa McCarthy, Chris O'Dowd, Kevin Kline, T... | United States | September 24, 2021 | 2021 | PG-13 | 104 min | Comedies, Dramas | A woman adjusting to life after a loss contend... |
## 转换时间维度——分成年、月
df["date_added"] = pd.to_datetime(df['date_added'])
df['year_added'] = df['date_added'].dt.year
df['month_added'] = df['date_added'].dt.month
##将duration列分成季与时长
df['season_count'] = df.apply(lambda x : str(x['duration']).split(" ")[0] if 'Season' in str(x['duration']) else "",axis = 1)
df['duration'] = df.apply(lambda x : str(x['duration']).split(" ")[0] if 'Season'not in str(x['duration']) else "",axis = 1)
df.head()
| show_id | type | title | director | cast | country | date_added | release_year | rating | duration | listed_in | description | year_added | month_added | season_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | s1 | Movie | Dick Johnson Is Dead | Kirsten Johnson | NaN | United States | 2021-09-25 | 2020 | PG-13 | 90 | Documentaries | As her father nears the end of his life, filmm... | 2021.0 | 9.0 | |
| 1 | s2 | TV Show | Blood & Water | NaN | Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... | South Africa | 2021-09-24 | 2021 | TV-MA | International TV Shows, TV Dramas, TV Mysteries | After crossing paths at a party, a Cape Town t... | 2021.0 | 9.0 | 2 | |
| 2 | s3 | TV Show | Ganglands | Julien Leclercq | Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi... | NaN | 2021-09-24 | 2021 | TV-MA | Crime TV Shows, International TV Shows, TV Act... | To protect his family from a powerful drug lor... | 2021.0 | 9.0 | 1 | |
| 3 | s4 | TV Show | Jailbirds New Orleans | NaN | NaN | NaN | 2021-09-24 | 2021 | TV-MA | Docuseries, Reality TV | Feuds, flirtations and toilet talk go down amo... | 2021.0 | 9.0 | 1 | |
| 4 | s5 | TV Show | Kota Factory | NaN | Mayur More, Jitendra Kumar, Ranjan Raj, Alam K... | India | 2021-09-24 | 2021 | TV-MA | International TV Shows, Romantic TV Shows, TV ... | In a city of coaching centers known to train I... | 2021.0 | 9.0 | 2 |
1、网飞上的影视内容类型占比
grouped = df['type'].value_counts().reset_index()
grouped = grouped.rename(columns = {'type' : "count", "index" : 'type'})## plot绘图
trace = go.Pie(labels=grouped['type'], values=grouped['count'], pull=[0.05, 0], marker=dict(colors=["#6ad49b", "#a678de"]))
layout = go.Layout(title="Netflix影视类型占比图", height=360, legend=dict(x=0.05, y=1.1))
fig = go.Figure(data = [trace], layout = layout)
iplot(fig)
grouped
| type | count | |
|---|---|---|
| 0 | Movie | 6131 |
| 1 | TV Show | 2676 |
电影占比69.6%，电视剧占比30.4%
2、不同类型年度增长趋势
#分类
df1 = df[df['type']== "TV Show"]
df2 = df[df['type']== "Movie"]
vc1 = df1['year_added'].value_counts().reset_index()
vc1 = vc1.rename(columns={'year_added':'count','index':'year_added'})
vc1['percent'] = vc1['count'].apply(lambda x:100*x/sum(vc1['count'])) #计算占比
vc1 = vc1.sort_values('year_added')
vc2 = df2['year_added'].value_counts().reset_index()
vc2 = vc2.rename(columns={'year_added':'count','index':'year_added'})
vc2['percent'] = vc2['count'].apply(lambda x:100*x/sum(vc2['count']))
vc2 = vc2.sort_values('year_added')
#plot绘图
trace1 = go.Scatter(x=vc1['year_added'], y=vc1["count"], name="TV Shows", marker=dict(color="#a678de"))
trace2 = go.Scatter(x=vc2['year_added'], y=vc2["count"], name="Movies", marker=dict(color="#6ad49b"))
data = [trace1, trace2]
layout = go.Layout(title="不同影视类型年度增长趋势图", legend=dict(x=0.1, y=1.1, orientation="h"))
fig = go.Figure(data, layout=layout)
fig.show()
从2013年开始，网飞上的内容开始迅速增多，其中电影的增长速度远高于电视剧，在2019年达到顶峰，然后略有滑落。
发行时间趋势
rs1 = df1['release_year'].value_counts().reset_index()
rs1 = rs1.rename(columns={'release_year':'count','index':'release_year'})
rs1['percent'] = rs1['count'].apply(lambda x : 100*x/sum(rs1['count']))
rs1 = rs1.sort_values('release_year')
rs2 = df2['release_year'].value_counts().reset_index()
rs2 = rs2.rename(columns={'release_year':'count','index':'release_year'})
rs2['percent'] = rs2['count'].apply(lambda x : 100*x/sum(rs2['count']))
rs2 = rs2.sort_values('release_year')
trace1 = go.Scatter(x=rs1['release_year'],y=rs1['count'],name='TV Shows', marker=dict(color="#a678de"))
trace2 = go.Scatter(x=rs2['release_year'],y=rs2['count'],name='Movies',marker=dict(color="#6ad49b"))
data = [trace1,trace2]
layout = go.Layout(title='不同类型影视发行时间趋势图',legend=dict(x=0.1,y=1.1,orientation='h'))
fig = go.Figure(data,layout=layout)
fig.show()
从年份细分到月份——每个月份发行影视内容
mth1 = df['month_added'].value_counts().reset_index()
mth1 = mth1.rename(columns={'month_added':'count','index':'month_added'})
mth1['percent'] = mth1['count'].apply(lambda x : 100*x/sum(mth1['count']))
mth1 = mth1.sort_values('month_added')
trace = go.Bar(x=mth1['month_added'],y=mth1['count'])
data = [trace]
layout = go.Layout(title='哪个月份发行电影/电视剧最多',legend=dict(x=0.1,y=1.1,orientation='h'))
fig = go.Figure(data,layout=layout)
fig.show()
总的来说，发行月份较为平稳，7月、12月份发行数量较多，可能是因为正处于寒暑假，是黄金时期，因此更多的影视作品选择在这段时间发布。
发行时间最早的影视
#电影
early = df.sort_values('release_year',ascending = True)
early = early[early['type']== "Movie"]
early[['title','release_year']].head(15)
| title | release_year | |
|---|---|---|
| 7790 | Prelude to War | 1942 |
| 8205 | The Battle of Midway | 1942 |
| 8660 | Undercover: How to Operate Behind Enemy Lines | 1943 |
| 8739 | Why We Fight: The Battle of Russia | 1943 |
| 8763 | WWII: Report from the Aleutians | 1943 |
| 8640 | Tunisian Victory | 1944 |
| 8436 | The Negro Soldier | 1944 |
| 8419 | The Memphis Belle: A Story of a\nFlying Fortress | 1944 |
| 7930 | San Pietro | 1945 |
| 7219 | Know Your Enemy - Japan | 1945 |
| 7575 | Nazi Concentration Camps | 1945 |
| 7294 | Let There Be Light | 1946 |
| 8587 | Thunderbolt | 1947 |
| 2375 | The Blazing Sun | 1954 |
| 1699 | White Christmas | 1954 |
#电视剧
early = df.sort_values('release_year',ascending = True)
early = early[early['type']== "TV Show"]
early[['title','release_year']].head(15)
影视作品在不同地区的分布情况
#国家代码
country_codes = {'afghanistan': 'AFG','albania': 'ALB','algeria': 'DZA','american samoa': 'ASM','andorra': 'AND','angola': 'AGO','anguilla': 'AIA','antigua and barbuda': 'ATG','argentina': 'ARG','armenia': 'ARM','aruba': 'ABW','australia': 'AUS','austria': 'AUT','azerbaijan': 'AZE','bahamas': 'BHM','bahrain': 'BHR','bangladesh': 'BGD','barbados': 'BRB','belarus': 'BLR','belgium': 'BEL','belize': 'BLZ','benin': 'BEN','bermuda': 'BMU','bhutan': 'BTN','bolivia': 'BOL','bosnia and herzegovina': 'BIH','botswana': 'BWA','brazil': 'BRA','british virgin islands': 'VGB','brunei': 'BRN','bulgaria': 'BGR','burkina faso': 'BFA','burma': 'MMR','burundi': 'BDI','cabo verde': 'CPV','cambodia': 'KHM','cameroon': 'CMR','canada': 'CAN','cayman islands': 'CYM','central african republic': 'CAF','chad': 'TCD','chile': 'CHL','china': 'CHN','colombia': 'COL','comoros': 'COM','congo democratic': 'COD','Congo republic': 'COG','cook islands': 'COK','costa rica': 'CRI',"cote d'ivoire": 'CIV','croatia': 'HRV','cuba': 'CUB','curacao': 'CUW','cyprus': 'CYP','czech republic': 'CZE','denmark': 'DNK','djibouti': 'DJI','dominica': 'DMA','dominican republic': 'DOM','ecuador': 'ECU','egypt': 'EGY','el salvador': 'SLV','equatorial guinea': 'GNQ','eritrea': 'ERI','estonia': 'EST','ethiopia': 'ETH','falkland islands': 'FLK','faroe islands': 'FRO','fiji': 'FJI','finland': 'FIN','france': 'FRA','french polynesia': 'PYF','gabon': 'GAB','gambia, the': 'GMB','georgia': 'GEO','germany': 'DEU','ghana': 'GHA','gibraltar': 'GIB','greece': 'GRC','greenland': 'GRL','grenada': 'GRD','guam': 'GUM','guatemala': 'GTM','guernsey': 'GGY','guinea-bissau': 'GNB','guinea': 'GIN','guyana': 'GUY','haiti': 'HTI','honduras': 'HND','hong kong': 'HKG','hungary': 'HUN','iceland': 'ISL','india': 'IND','indonesia': 'IDN','iran': 'IRN','iraq': 'IRQ','ireland': 'IRL','isle of man': 'IMN','israel': 'ISR','italy': 'ITA','jamaica': 'JAM','japan': 'JPN','jersey': 'JEY','jordan': 'JOR','kazakhstan': 'KAZ','kenya': 'KEN','kiribati': 'KIR','north korea': 'PRK','south korea': 'KOR','kosovo': 'KSV','kuwait': 'KWT','kyrgyzstan': 'KGZ','laos': 'LAO','latvia': 'LVA','lebanon': 'LBN','lesotho': 'LSO','liberia': 'LBR','libya': 'LBY','liechtenstein': 'LIE','lithuania': 'LTU','luxembourg': 'LUX','macau': 'MAC','macedonia': 'MKD','madagascar': 'MDG','malawi': 'MWI','malaysia': 'MYS','maldives': 'MDV','mali': 'MLI','malta': 'MLT','marshall islands': 'MHL','mauritania': 'MRT','mauritius': 'MUS','mexico': 'MEX','micronesia': 'FSM','moldova': 'MDA','monaco': 'MCO','mongolia': 'MNG','montenegro': 'MNE','morocco': 'MAR','mozambique': 'MOZ','namibia': 'NAM','nepal': 'NPL','netherlands': 'NLD','new caledonia': 'NCL','new zealand': 'NZL','nicaragua': 'NIC','nigeria': 'NGA','niger': 'NER','niue': 'NIU','northern mariana islands': 'MNP','norway': 'NOR','oman': 'OMN','pakistan': 'PAK','palau': 'PLW','panama': 'PAN','papua new guinea': 'PNG','paraguay': 'PRY','peru': 'PER','philippines': 'PHL','poland': 'POL','portugal': 'PRT','puerto rico': 'PRI','qatar': 'QAT','romania': 'ROU','russia': 'RUS','rwanda': 'RWA','saint kitts and nevis': 'KNA','saint lucia': 'LCA','saint martin': 'MAF','saint pierre and miquelon': 'SPM','saint vincent and the grenadines': 'VCT','samoa': 'WSM','san marino': 'SMR','sao tome and principe': 'STP','saudi arabia': 'SAU','senegal': 'SEN','serbia': 'SRB','seychelles': 'SYC','sierra leone': 'SLE','singapore': 'SGP','sint maarten': 'SXM','slovakia': 'SVK','slovenia': 'SVN','solomon islands': 'SLB','somalia': 'SOM','south africa': 'ZAF','south sudan': 'SSD','spain': 'ESP','sri lanka': 'LKA','sudan': 'SDN','suriname': 'SUR','swaziland': 'SWZ','sweden': 'SWE','switzerland': 'CHE','syria': 'SYR','taiwan': 'TWN','tajikistan': 'TJK','tanzania': 'TZA','thailand': 'THA','timor-leste': 'TLS','togo': 'TGO','tonga': 'TON','trinidad and tobago': 'TTO','tunisia': 'TUN','turkey': 'TUR','turkmenistan': 'TKM','tuvalu': 'TUV','uganda': 'UGA','ukraine': 'UKR','united arab emirates': 'ARE','united kingdom': 'GBR','united states': 'USA','uruguay': 'URY','uzbekistan': 'UZB','vanuatu': 'VUT','venezuela': 'VEN','vietnam': 'VNM','virgin islands': 'VGB','west bank': 'WBG','yemen': 'YEM','zambia': 'ZMB','zimbabwe': 'ZWE'}## countries
from collections import Counter
colorscale = ["#f7fbff", "#ebf3fb", "#deebf7", "#d2e3f3", "#c6dbef", "#b3d2e9", "#9ecae1","#85bcdb", "#6baed6", "#57a0ce", "#4292c6", "#3082be", "#2171b5", "#1361a9","#08519c", "#0b4083", "#08306b"
]#统计每个国家发行的影视作品数量
def geoplot(ddf):country_with_code, country = {}, {}shows_countries = ", ".join(ddf['country'].dropna()).split(", ")for c,v in dict(Counter(shows_countries)).items():code = ""if c.lower() in country_codes:code = country_codes[c.lower()]country_with_code[code] = vcountry[c] = vdata = [dict(type = 'choropleth',#分级统计图locations = list(country_with_code.keys()),#地图z = list(country_with_code.values()),colorscale = [[0,"rgb(5, 10, 172)"],[0.65,"rgb(40, 60, 190)"],[0.75,"rgb(70, 100, 245)"],\[0.80,"rgb(90, 120, 245)"],[0.9,"rgb(106, 137, 247)"],[1,"rgb(220, 220, 220)"]],autocolorscale = False,reversescale = True,marker = dict(line = dict (color = 'gray',width = 0.5) ),colorbar = dict(autotick = False,title = ''),) ]layout = dict(title = '',geo = dict(showframe = False,showcoastlines = False,projection = dict(type = 'Mercator')))fig = dict( data=data, layout=layout )iplot( fig, validate=False, filename='d3-world-map' )return countrycountry_vals = geoplot(df)
tabs = Counter(country_vals).most_common(25)labels = [_[0] for _ in tabs][::-1]
values = [_[1] for _ in tabs][::-1]
trace1 = go.Bar(y=labels, x=values, orientation="h", name="", marker=dict(color="#a678de"))data = [trace1]
layout = go.Layout(title="Countries with most content", height=700, legend=dict(x=0.1, y=1.1, orientation="h"))
fig = go.Figure(data, layout=layout)
fig.show()
不同地区发行最多的电影类型
categories = ", ".join(df2['listed_in']).split(", ")
counter_list = Counter(categories).most_common(50)
label = [_[0] for _ in counter_list][::-1]
values = [_[1] for _ in counter_list][::-1]trace = go.Bar(y=label,x=values,orientation="h",name="Movie",marker=dict(color = "#a678de"))
data = [trace]
layout = go.Layout(title="哪种类型的电影最多",legend=dict(x=0.1,y=1.1,orientation="h"))
fig = go.Figure(data,layout=layout)
fig.show()
##地区分布图
def country_type(country,flag):if flag == "Movie":temp = df[df['duration'] != ""]else:temp = df[df['season_count'] != ""]total_categories = ",".join(temp['listed_in'].fillna("")).split(",")temp = temp[temp['country'].fillna("").apply(lambda x : country in x)]categories = ",".join(temp['listed_in'].fillna("")).split(",")cat = Counter(categories).most_common(5)labels,values = [_[0] for _ in cat],[_[1] for _ in cat]trace = go.Bar(y=labels[::-1],x=values[::-1],orientation="h",marker=dict(color="#a678de"))total_values = []for label in labels:total_values.append(Counter(total_categories)[label])trace1 = go.Bar(y=labels[::-1],x=total_values[::-1],orientation="h",marker=dict(color="orange"))data = [trace,trace1]return datafrom plotly.subplots import make_subplots
traces = []
flag="Movie"
titles = ["United States"," ","India"," ","United Kingdom","Canada"," ", "Spain"," ", "Japan",]
for title in titles:if title != "":traces.append(country_type(title,flag))fig = make_subplots(rows=3, cols=5, subplot_titles=titles)
fig.add_trace(traces[0][0], 1,1)
fig.add_trace(traces[0][1], 1,1)
fig.add_trace(traces[1][0], 1,3)
fig.add_trace(traces[1][1], 1,3)
fig.add_trace(traces[2][0], 1,5)
fig.add_trace(traces[2][1], 1,5)
fig.add_trace(traces[3][0], 2,1)
fig.add_trace(traces[3][1], 2,1)
fig.add_trace(traces[4][0], 2,3)
fig.add_trace(traces[4][1], 2,3)
fig.add_trace(traces[5][0], 2,5)
fig.add_trace(traces[5][1], 2,5)
fig.add_trace(traces[6][0], 3,1)
fig.add_trace(traces[6][1], 3,1)
fig.add_trace(traces[7][0], 3,3)
fig.add_trace(traces[7][1], 3,3)
fig.add_trace(traces[8][0], 3,5)
fig.add_trace(traces[8][1], 3,5)fig.update_layout(title='不同国家发行影视作品类型',barmode='stack',height=1200, showlegend=False)
fig.show()
可以看到网络电影最多，这符合网飞流媒体区别于院线传统媒体的特点，而且网飞很多都是自制影视作品，因此网络电影占比高是正常的，此外，戏剧、喜剧、纪录片和动作片的数量占比较高。
对各个国家发行的数量排名前5的影视作品进行比较，纪录片在美国和英国的发行量较高，中国的动作片发行数量相对较高，而日本的动画片占比很高，几乎所有的动漫都由日本发行，加拿大的儿童家庭片占比较高，可以看出不同国家发行的作品类型反映了各地的特色。
kaggle数分项目 | netfix影视内容分析相关推荐
- 数分项目《泰坦尼克》——Task2
目录 4.数据清洗与特征处理 4.1缺失值观察与处理 4.1.1 任务一:缺失值观察 4.1.2 任务二:对缺失值进行处理 4.2重复值观察与处理 4.2.1 任务一:查看数据中的重复值 4.2.2 ...
- netfix影视资源数据分析
目录 介绍 导入库 读取数据 查看数据 数据可视化 电影与电视的对比 哪些月份添加影视资源到netfix的最多 电影适合观看的年龄级别 发布电影最多的15个年份 电影制作最多的10个国家 电影时长分析 ...
- 灰色项目之影视卡项目背后的套路与行业现状
我朋友圈里至少不下五个人在做这个项目,影视卡项目,其中还不乏我认识的朋友.曾经也有人问过我,这个项目怎么样,好做吗?赚钱不?其实我想说,这个是灰色项目,不管它赚不赚钱,这都不是我要考虑的,选择项目首先 ...
- python sns绘制回归线_Python数分实战：员工流失情况预测
在很久之前,我有写一个Excel数据分析的实战项目,不晓得大家还记不记得,感兴趣的童鞋可以回看: A九姑娘:Excel数分实战:员工流失率分析zhuanlan.zhihu.com 本次的项目数据依旧 ...
- 一线数分师天天被喷不值钱，数据分析师如何“刷存在感”？
在职场论坛里,常常能看到数分人发帖吐槽怀疑岗位的价值:钱少也就算了,天天当取数工具人,忙碌劳累,但产出的价值难以量化,感觉自己在公司没啥存在感. 更让人不理解的是,现在的产品.运营.业务也开始做数据分 ...
- 大数据千亿级离线数仓项目第一天 环境部署和etl
千亿级数仓项目第01天讲义 课程目标 了解大数据离线数仓架构 了解项目部署环境(数据规模和集群规模) 掌握ETL工具 Kettle常用组件的使用 能够掌握kettle作业与转换区别以及linux部署 ...
- 数分学习笔记 vol.1 ＜游戏数分基本工作内容＞
写作目的 快要脱离学生身份了,但为了在未来的工作中不被淘汰,个人认为继续学习是必须的.因此写下了这个博客,希望自己能在工作中不断积累,总结经验,不断提升. 所有内容仅代表个人的想法和感悟,如有不同意见 ...
- kaggle住房预测项目——第2部分(bagging)
kaggle住房预测项目--第2部分(bagging) 基线模型 import xgboost as xgb import copy import datetime,time from sklearn ...
- 电商离线数仓项目实战（下）
电商离线数仓项目实战(下) 电商分析--核心交易 文章目录 电商离线数仓项目实战(下) 电商分析--核心交易 一.业务需求 二.业务数据库表结构 1. 数据库表之间的联系 img 2. 业务数据库-- ...
最新文章
- C++中public、protect和private用法区别
- vue全家桶 ---axios的使用和二次封装
- 初始化全局变量实例说明C语言中初始化和未初始化的全局变量所在不同的段
- 微服务实战（四）：服务发现的可行方案以及实践案例
- Python3-join()和split()的用法
- MyEcplise中SpringBoot怎样定制启动banner？
- Hibernate3 jar包的作用[转]
- 设计模式之一：单例模式SingleTon
- C#积木游戏(改编自DevExpress GridTetris)
- C语言程序设计第五版 谭浩强 第五版课后答案
- linux服务器上的cad病毒,LibreCAD_LibreCAD下载[2021官方最新版]LibreCAD安全下载_ 极速下载...
- 特洛伊木马程序_特洛伊木马Dridex的新变种可避免反病毒软件的检测
- web打印的最佳方案
- 【STM32】可变焦、聚焦摄像头驱动电路与驱动程序——两相四线步进电机驱动
- springboot 加载一个properties文件转换为对象、List和Map数据结构
- 知识表示的方法（2）——框架表示法
- 软件测试-Mysql数据库3
- 人人看得懂的ChatGPT技术原理解析
- 2星|稻盛和夫《经营十二条》：内容太水，看目录就可以了
- 忘记历史就意味着背叛