Python大数据开发讲义

Python大数据开发讲义Python大数据开发讲义第1页4数据分析3数据描述1数据搜集2数据整理

大数据处理过程

2Python大数据开发讲义第2页便捷数据获取Python大数据编程Python大数据开发讲义第3页用Python获取数据当地数据怎样获取?文件打开，读写和关闭• 文件打开• 读文件写文件• 文件关闭4Python大数据开发讲义第4页用Python获取数据网络数据怎样获取?抓取网页，解析网页内容urlliburllib2httplib•httplib25Python

3中被urllib.request代替Python

3中被http.client代替Python大数据开发讲义第5页yahoo财经数据/q/cp?s=%5EDJI+Componen

tPython大数据开发讲义第6页利用urllib库获取yahoo财经数据#Filename:dji.pyimporturllibimportredBytes=urllib.request.urlopen('http:///q/cp?s=%5EDJI+Components').read()

dStr=dBytes.decode('GBK')#在python3中urllib.read()返回bytes对象而非str，语句功效是将dBytes转换成Strm=re.findall('<tr><tdclass="yfnc_tabledata1"><b><ahref=".*?">(.*?)</a></b></td><tdclass="yfnc_tabledata1">(.*?)</td>.*?<b>(.*?)</b>.*?</tr>',dStr)ifm: print(m)

print('\n‘)

print(len(m))else: print('notmatch‘)Python大数据开发讲义第7页数据形式• 包含多个字符串（dji）'AXP','AmericanExpressCompany',

'86.40''BA','TheBoeingCompany',

'122.24''CAT','CaterpillarInc.',

'99.44''CSCO','CiscoSystems,Inc.',

'23.78''CVX','ChevronCorporation',

'115.91'– …Python大数据开发讲义第8页便捷网络数据是否能够简单方便而且快速方式获得雅虎财经上各上市公司股票历史数据？File#Filename:

quotes.pyfrommatplotlib.financeimportquotes_historical_yahoo_ochlfromdatetimeimport

dateimportpandasas

pdtoday=

date.today()start=(today.year-1,today.month,

today.day)quotes=quotes_historical_yahoo_ochl('AXP',start,today)df=

pd.DataFrame(quotes)print

dfPython大数据开发讲义第9页便捷网络数据quotes内容日期收盘价开盘价最高价最低价成交量Python大数据开发讲义第10页便捷网络数据自然语言工具包NLTK古腾堡语料库• 布朗语料库• 路透社语料库• 网络和聊天文本• …>>>fromnltk.corpusimport

gutenberg>>>import

nltk>>>print

gutenberg.fileids()[u'austen-emma.txt',u'austen-persuasion.txt',u'austen-sense.txt',u'bible-kjv.txt',u'blake-poems.txt',u'bryant-stories.txt',u'burgess-busterbrown.txt',u'carroll-alice.txt',u'chesterton-ball.txt',u'chesterton-brown.txt',u'chesterton-thursday.txt',u'edgeworth-parents.txt',u'melville-moby_dick.txt',u'milton-paradise.txt',u'shakespeare-caesar.txt',u'shakespeare-hamlet.txt',u'shakespeare-macbeth.txt',u'whitman-leaves.txt']>>>texts=

gutenberg.words('shakespeare-hamlet.txt')[u'[',u'The',u'Tragedie',u'of',u'Hamlet',u'by',

...]Sourcebrown需要先执行nltk.download()下载某一个或多个包，若下载失败，能够在官网（/nltk_data/）

•单独下载后放到当地python目录nltk_data\corpora下Python大数据开发讲义第11页Python大数据开发讲义第12页数据准备Python大数据编程Python大数据开发讲义第13页数据形式30支成份股（dji）股票数据逻辑结构企业代码企业名最近一次成交价美国运通企业（quotes）股票详细数据逻辑结构日期开盘价收盘价最高价最低价成交量Python大数据开发讲义第14页数据整理quotes数据加属性名File#Filename:quotesproc.pyfrommatplotlib.financeimportquotes_historical_yahoo_ochlfromdatetimeimportdateimportpandasas

pdtoday=

date.today()start=(today.year-1,today.month,

today.day)quotes=quotes_historical_yahoo_ochl('AXP',start,

today)fields=['date','open','close','high','low','volume']quotesdf=pd.DataFrame(quotes,columns=fields)print

quotesdfPython大数据开发讲义第15页数据整理dji数据：加属性名codeAXPnamelasttradeBACAT…XOMquotes数据：加属性名dateopenclosehighlowvolume735190.0735191.0735192.0…735551.0Python大数据开发讲义第16页数据整理用1,2,…作为索引quotesdf=pd.DataFrame(quotes,columns=

fields)quotesdf=pd.DataFrame(quotes,index=range(1,len(quotes)+1),columns=

fields)Python大数据开发讲义第17页数据整理假如能够直接用date作为索引，quotes时间能否转换成常规形式（以下列图中效果）？Source>>>fromdatetimeimport

date>>>firstday=

date.fromordinal(735190)>>>lastday=

date.fromordinal(735551)>>>

firstdaydatetime.date(,11,

18)>>>lastdaydatetime.date(,11,

14)Python大数据开发讲义第18页时间序列#Filename:

quotesproc.pyfrommatplotlib.financeimport

quotes_historical_yahoo_ochlfromdatetimeimportdatefromdatetimeimportdatetimeimportpandasas

pdtoday=

date.today()start=(today.year-1,today.month,

today.day)quotes=quotes_historical_yahoo_ochl('AXP',start,today)fields=

['date','open','close','high','low','volume']list1=

[]foriin

range(0,len(quotes)):x=date.fromordinal(int(quotes[i][0]))y=datetime.strftime(x,'%Y-%m-%d')list1.append(y)quotesdf=pd.DataFrame(quotes,index=list1,columns=

fields)quotesdf=quotesdf.drop(['date'],axis=

1)print

quotesdfFile转换成常规时间转换成固定格式删除原date列Python大数据开发讲义第19页创建时间序列>>>importpandasas

pd>>>dates=pd.date_range('1001',

periods=7)>>>

dates<class

'pandas.tseries.index.DatetimeIndex'>[-10-01,...,

-10-07]Length:7,Freq:D,Timezone:

None>>>importnumpyas

np>>>dates=pd.DataFrame(np.random.randn(7,3),index=dates,columns=

list('ABC'))>>>

datesA B C-10-011.302600-1.214708

1.411628-10-02-0.5123432.277474

0.403811-10-03-0.788498-0.217161

0.173284-10-041.042167-0.453329

-2.107163-10-05-1.6280751.663377

0.943582-10-06-0.0910340.335884

2.455431-10-07-0.679055-0.865973

0.246970[7rowsx3

columns]SourcePython大数据开发讲义第20页数据显示Python大数据编程Python大数据开发讲义第21页数据显示djidfquotesdfPython大数据开发讲义第22页数据显示Source>>>

djidf.indexInt64Index([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,

20,21,22,23,24,25,26,27,28,29],

dtype='int64')>>>

djidf.columnsIndex([u'code',u'name',u'lasttrade'],

dtype='object')>>>

dijdf.valuesarray([['AXP','AmericanExpressCompany','90.67'],['BA','TheBoeingCompany',

'128.86'],…['XOM','ExxonMobilCorporation','95.09']],

dtype=object)>>>

djidf.describe<boundmethodDataFrame.describe

of

code name lasttrade0 AXP American

Express

Company 90.671…29BAXOMTheBoeing

CompanyExxonMobil

Corporation128.8695.09显示方式：• 显示索引• 显示列名• 显示数据值• 显示数据描述Python大数据开发讲义第23页数据显示Source>>>

quotesdf.indexIndex([u'-11-18',u'-11-19',u'-11-20',

u'-11-21',u'-11-22',u'-11-25',u'-11-26',

u'-11-27',…-04-08',u'-04-09',u'-04-10',u'-04-11',

...],dtype='object')索引格式Python大数据开发讲义第24页数据显示>>>

djidf.head(5)codename0 AXPAmericanExpress

Company1 BA2 CATCSCOCVXTheBoeing

CompanyCaterpillar

Inc.Cisco

Systems,Inc.Chevron

Corporationlasttrade90.67128.86101.3426.32116.32[5rowsx3

columns]>>>

djidf.tail(5)codename

lasttrade25 UTXUnitedTechnologies

Corporation26 V Visa

Inc.27

VZVerizonCommunications

Inc.WMTXOMWal-MartStores

Inc.ExxonMobil

Corporation107.45248.8451.5082.9695.09[5rowsx3

columns]Sourcedf[:5]df[25:]显示方式：• 显示行−

专用方式−

切片查看道琼斯工业股中前5只和后5只股票基础信息？Python大数据开发讲义第25页数据选择Python大数据编程Python大数据开发讲义第26页数据选择选择方式：• 选择行• 选择列• 选择区域• 筛选（条件选择）Python大数据开发讲义第27页数据选择-12-02open85.092126close84.37high85.596624low84.241402volume3620800-12-0383.97698983.7084.41225683.2944103546100-12-0483.30312383.5984.32203182.8579693579700-12-0583.36290683.6384.07515683.2441983677800-12-0684.66368085.0085.15826884.4262772666600[5rowsx5columns]Source>>>

quotesdf[u'-12-02':u'-12-06']选择方式：• 选择行−

切片−

索引美国运通企业12月2日至12月6日间股票交易信息？Python大数据开发讲义第28页数据选择AXPBACAT012…29

XOMName:code,dtype:

object>>>

djidf.code01AXPBACAT2…29

XOMName:code,dtype:objectSource>>>

djidf['code']选择方式：• 选择列−

列名不支持djidf['code','lasttrade']djidf['code':'lasttrade']道琼斯工业股公司代码？Python大数据开发讲义第29页数据选择选择方式：• 行、列−

标签label（loc）codenamelasttrade1 BATheBoeing

Company128.862 CATCaterpillar

Inc.101.343 CSCOCiscoSystems,

Inc.26.324 CVXChevron

Corporation116.325 DDE.I.duPontdeNemoursandCompany70.80[5rowsx3

columns]>>>

djidf.loc[:,['code','lasttrade']]code lasttrade0 AXP1 BA90.67128.862 CAT 101.34…29

XOM

95.09Source>>>

djidf.loc[1:5,]道琼斯工业股中标号是1至5股票信息以及全部股票代码和最近一次交易价？[30rowsx2

columns]Python大数据开发讲义第30页数据选择选择方式：• 行和列区域标签label（loc）• 单个值at3

CSCOcode

lasttrade1 BA 128.862 CAT 101.3426.324 CVX 116.325 DD 70.80[5rowsx2

columns]>>>

djidf.loc[1,'lasttrade']'128.86‘>>>djidf.at[1,'lasttrade']'128.86'Source>>>

djidf.loc[1:5,['code','lasttrade']]道琼斯工业股中标号是1至5股票代码和最近一次交易价?标号是1股票最近一次交易价？Python大数据开发讲义第31页数据选择选择方式：• 行、列和区域− 用iloc（位置）• 取某个值− iat>>>

djidf.loc[1:5,['code','lasttrade']]2 CATCSCOCVXcode

lasttrade1

BA

128.86101.3426.32116.325 DD 70.80SourceSource>>>

djidf.iloc[1:6,[0,2]]code

lasttrade1 BA 128.862 CAT101.343

CSCO26.324 CVX116.325 DD70.80>>>djidf.loc[1,'lasttrade']'128.86'>>>

djidf.at[1,'lasttrade']'128.86'Source>>>

djidf.iloc[1,2]'128.86'>>>djidf.iat[1,2]'128.86'SourcePython大数据开发讲义第32页数据选择>>>quotesdf[quotesdf.index>=

u'-01-01']open close highlow volume-01-0289.92443888.4990.10250688.420751511-01-0388.18637788.7789.10632587.6719983888500-01-0688.73000088.7389.27405288.4134602844700…-03-2889.53155489.7290.81100289.2637633138900...... .........[221rowsx5

columns]>>>quotesdf[(quotesdf.index>=u'-01-01')&(quotesdf.close>=

95)]openclosehighlowvolume-06-0994.53282095.0295.32821694.1052953825200-06-1894.20466295.0195.03982793.5385182454800-07-0395.03149295.2995.38942694.6735581633800[3rowsx5columns]Source美国运通企业股票信息？深入寻找美国运通企业收盘价大于等于95统计？选择方式：• 条件筛选Python大数据开发讲义第33页简单统计与处理Python大数据编程Python大数据开发讲义第34页简单统计与筛选1 TheBoeingCompany8 TheGoldmanSachsGroup,

Inc.10 InternationalBusinessMachines

Corporation16 3M

Company26 Visa

Inc.Name:name,dtype:

objectSource>>>djidf.mean(columns='lasttrade')lasttrade 91.533667dtype:

float64>>>djidf[djidf.lasttrade>=

120].name求道琼斯工业股中30只股票最近一次成交价平均值？股票最近一次成交价大于等于120企业名？Python大数据开发讲义第35页简单统计与筛选Source>>>len(quotesdf[quotesdf.close>

quotesdf.open])131>>>len(quotesdf)-131120统计美国运通公司近一年股票涨和跌分别天数？统计美国运通公司近一年相邻两天收盘价涨跌情况？Source>>>status=

np.sign(np.diff(quotesdf.close))>>>

statusarray([1.,-1.,1.,-1.,1.,1.,1.,1.,-1.,-1.,-1.,1.,

1.,…-1.,-1.,

-1.])>>>status[np.where(status==1.)].size130>>>status[np.where(status==-1.)].size120Python大数据开发讲义第36页排序>>>djidf.sort(columns=

'lasttrade')codename

lasttrade3 CSCO7 GECiscoSystems,Inc.GeneralElectric

Company26.3226.4620PFEPfizer

Inc.30.3411INTCIntel

Corporation33.95…8GSTheGoldmanSachsGroup,

Inc.189.9826VVisa

Inc.248.84[30rowsx3

columns]>>>djidf.sort(columns=

'lasttrade')[27:].name10 InternationalBusinessMachines

Corporation8 TheGoldmanSachsGroup,

Inc.26 Visa

Inc.Name:name,dtype:

objectSource按最近一次成交价对30只道琼斯工业股股票进行排序。依据排序结果列出前三甲企业名。能够添加sort()函数ascending属性控制次序/逆序排序，默认该属性=True，即次序排列DataFramesort()函数已不推荐使用，相同功效推荐使用sort_index()函数Python大数据开发讲义第37页计数统计openclosehighlowvolume-01-0289.92443888.4990.10250688.420751511-01-0388.18637788.7789.10632587.6719983888500…-01-3085.74139385.9186.26704985.3645084259000-01-3184.57785984.3285.20267284.2109064778000[21rowsx5

columns]>>>

len(t)21Source>>>t=quotesdf[(quotesdf.index>='-01-01')&(quotesdf.index<

'-02-01')]>>>

t统计1月份股票开盘天数？Python大数据开发讲义第38页计数统计统计近一年每个月股票开盘天数？File#Filename:quotesmonth.pyimport

time…listtemp=

[]foriin

range(0,len(quotesdf)):temp=time.strptime(quotesdf.index[i],"%Y-%m-%d")listtemp.append(temp.tm_mon)print

listtemptempdf=

quotesdf.copy()tempdf['month']=

listtempprint

tempdf['month'].value_counts()Output:10 237 2212 219 218 216 215 214 213 211 2111 192 19dtype:

int64Python大数据开发讲义第39页GROUPINGPython大数据编程Python大数据开发讲义第40页分组Grouping次序① Splitting② Applying③ CombiningPython大数据开发讲义第41页分组统计近一年每个月股票开盘天数？Source>>>

tempdf.groupby('month').count()openclosehigh

low

volume monthmonth1 21 21 21 21 21 212 19 19 19 19 19 193 21 21 21 21 21 21…10 23 23 23 23 23 2311 20 20 20 20 20 2012 21 21 21 21 21 21[12rowsx6

columns]>>>

tempdf.groupby('month').count().month假如没有特殊处理话则没有month这一列数据假如没有month这一列数据，则此处month需要改成其它属性名如openOutput:month1 212 193 214 215 216 217 228 219 2110 2311 2012 21Name:

month,dtype:

int64Python大数据开发讲义第42页分组统计近一年每个月总成交量？>>>tempdf.groupby('month').sum().volumemonth1104874000276173800371488400484786400…98534140010120822100Sourcemean()min()max()…11679063001267589400Name:volume,dtype:

float64Python大数据开发讲义第43页分组假如更高效统计近一年每个月总成交量？…123101048740007617380071488400120822100Source>>>g=

tempdf.groupby('month')>>>gvolume=

g['volume']>>>print

gvolume.sum()month11679063001267589400Name:volume,dtype:

float64Python大数据开发讲义第44页MERGEPython大数据编程Python大数据开发讲义第45页合并Merge形式Append加行到DataFrameConcat连接pandas对象Join− SQL类型连接Python大数据开发讲义第46页Appendlow

volume-11-1881.95448781.4582.083082

81.2719463104800-11-1981.49086381.5781.985467

81.3820512775000[2rowsx5

columns]>>>q=

quotesdf[u'-01-01':u'-01-05']>>>

qopen close high low volume-01-0289.92443888.4990.10250688.420751

511-01-0388.18637788.7789.10632587.671998

3888500[2rowsx5

columns]>>>

p.append(q)openclosehighlowvolume-11-1881.95448781.4582.08308281.2719463104800-11-1981.49086381.5781.98546781.3820512775000-01-0289.92443888.4990.10250688.420751511-01-0388.18637788.7789.10632587.6719983888500[4rowsx5columns]Source>>>p=

quotesdf[:2]>>>

popen close high把美国运通企业1月1日至1月5日间股票交易信息合并到近一年中前两天股票信息中？Python大数据开发讲义第47页Concat将美国运通企业近一年股票数据中前5个和后5个合并。open

close high low volume

month-11-1881.95448781.4582.08308281.271946

310480011-11-1981.49086381.5781.98546781.382051277500011-11-2082.06231781.3682.51734081.073138351050011-11-2181.74415183.0583.09946481.684795435520011-11-2283.01924982.9583.02914182.475152269030011-11-1192.28000091.7492.59000091.490000271980011-11-1291.16000091.5591.67000091.100000382520011-11-1391.70000091.0791.70000090.940000363790011-11-1491.07000090.6791.24000090.35000023616001111-11-1790.24000090.1390.26000089.650000

2620500[20rowsx6

columns]Source>>>pieces=[tempdf[:5],tempdf[len(tempdf)-5:]]>>>

pd.concat(pieces)Python大数据开发讲义第48页Concat两个不一样逻辑结构对象能否连接？closehighlowmonthopenvolume081.4582.08308281.271946NaN81.9544873104800181.5781.98546781.382051NaN81.4908632775000281.3682.51734081.073138NaN82.0623173510500381.4582.08308281.2719461181.9544873104800481.5781.98546781.3820511181.4908632775000581.3682.51734081.0731381182.0623173510500[6rowsx6

columns]Source>>>piece1=

quotesdf[:3]>>>piece2=

tempdf[:3]>>>pd.concat([piece1,piece2],ignore_index=

True)objsaxisjoinjoin_axeskeyslevelsnamesverify_integrityignore_indexPython大数据开发讲义第49页JoincodemonthvolumeAXPAXPKOKOcodenamelasttradeAXPKOcodenamelasttrademonthvolumeAXPAXPKOKOPython大数据开发讲义第50页Join将美国运通企业和可口可乐企业近一年中每个月交易总量表（包含企业代码）与30只道琼斯成份股股票信息合并。code|name|month|volumesPython大数据开发讲义第51页Joincodename

lasttradevolume

month0AXPAmericanExpress

Company90.1310487400011AXPAmericanExpress

Company90.137617380022AXPAmericanExpress

Company90.137148840033AXPAmericanExpress

Company90.138478640044AXPAmericanExpress

Company9019KOTheCoca-Cola

Company42.92247854800820KOTheCoca-Cola

Company42.92314443100921KOTheCoca-Cola

Company42.925295419001022KOTheCoca-Cola

Company42.922658630001123KOTheCoca-Cola

Company42.9230114450012[24rowsx5

columns]>>>pd.merge(djidf,AKdf,on='code').drop(['lasttrade'],axis=

1)Source>>>pd.merge(djidf,AKdf,on=

'code')Python大数据开发讲义第52页merge函数参数leftrighthowonleft_onright_onleft_indexright_indexsortsuffixescopyPython大数据开发讲义第53页条件Python大数据编程Python大数据开发讲义第54页if

语句File#Filename:

ifpro.pysd1=

3sd2=

3ifsd1==

sd2:print"thesquare'sareais:%d"%

(sd1*sd2)ifexpression

:expr_true_suite语

法条件表示式：• 比较运算符• 组员运算符• 逻辑运算符expressionexpression条件

为True时执行代码块• 代码块必须缩进（通常为4个空格）expr_true_suitePython大数据开发讲义第55页else

语句ifexpression:expr_true_suiteelse:expr_false_suite语

法expression条件为False

时

执

行

代

码块• 代码块必须缩进• else语句不缩进expr_false_suiteFile#Filename:

elsepro.pysd1=int(raw_input('thefirstside:'))sd2=int(raw_input('thesecond

side:'))ifsd1==

sd2:print"thesquare'sareais:%d"%(sd1*sd2)else:print"therectangle'sareais:%d"

%(sd1*sd2)Inputand

Outputthefirstside:4thesecond

side:4thesquare'sarea

is:16Python大数据开发讲义第56页elif

语句ifexpression

:expr_true_suiteelifexpression2:expr2_true_suite::elifexpressionN:exprN_true_suiteelse:none_of_the_above_suite语

法expression2为True时执行代码块expr2_true_suiteexpressionN为

True时执行代码块exprN_true_suitenone_of_the_above_suite是以上全部条件都不满足时执行代码块elsePython大数据开发讲义第57页elif

语句File#Filename:

elifpro.pyk=raw_input('inputtheindexof

shape:')ifk==

'1':print

'circle'elifk==

'2':print

'oval'elifk==

'3':print

'rectangle'elifk==

'4':print'triangle'else:print'youinputtheinvalid

number'InputandOutputinputtheindexof

shape:3rectangleInputandOutputinputtheindexof

shape:8youinputtheinvalid

numberPython大数据开发讲义第58页条件嵌套File#Filename:

ifnestpro.pyk=raw_input('inputtheindexofshape:')ifk==

'1':print

'circle'elifk==

'2':print

'oval'elifk==

'3':sd1=int(raw_input('thefirstside:'))sd2=int(raw_input(:'thesecondside'))ifsd1==

sd2:print"thesquare'sareais:%d"%(sd1*sd2)else:print"therectangle'sareais:%d"%(sd1*sd2)print

'rectangle'elifk==

'4':print

'triangle'else:print'youinputtheinvalid

number'• 同等缩进为同一条件结构InputandOutputinputtheindexofshape:3thefirst

side:3thesecond

side:4therectangle'sareais:12InputandOutputinputtheindexof

shape:2ovalPython大数据开发讲义第59页猜数字游戏•程序随机产生一个0~300间整数，玩家竞猜，系统给出“猜中”、“太大了”或“太小了”提醒。File#Filename:

guessnum1.pyfromrandomimport

randintx=randint(0,

300)print'Pleaseinputanumberbetween

0~300:'digit=

input()ifdigit==x:print

'Bingo!'elifdigit>

x:print'Toolarge,pleasetryagain.'else:print'Toosmall,pleasetry

again.'Python大数据开发讲义第60页RANGE和XRANGEPython大数据编程Python大数据开发讲义第61页range()语

法• 起始值（包含）start• 终值（不包含）end• 步长（不能为0）step• 不包含end值range(start,end,

step=1)• 缺省step值为1range(start,

end)缺省了start值为0，step为1range

(end)range(start,end,step=1)range(start,

end)range

(end)• 生成一个真实列表Source>>>

range(3,11,2)[3,5,7,

9]>>>

range(3,11)[3,4,5,6,7,8,9,

10]>>>

range(11)[0,1,2,3,4,5,6,7,8,9,

10]Python大数据开发讲义第62页xrange()异同range()xrange()语法一样返回列表生成器生成真实列表用多少生成多少>>>foriinxrange(3,11,2):print

iSource>>>xrange(3,11,2)xrange(3,11,

2)>>>print

xrange(3,11,2)xrange(3,11,

2)Output:3579在Python

3.x中不支持xrange()函数，都使用range()函数，其返回值为range对象，而且需要显式调用，常见list(range(10))形式Python大数据开发讲义第63页循环Python大数据编程Python大数据开发讲义第64页while

循环Source>>>sumA=

0>>>j=

1>>>whilej<

10:sumA+=

jj+=

1>>>

sumA45>>>

j10whileexpression:suite_to_repeat语法• 条件表示式当

expression

值为

True时

执

行

suite_to_repeat代码块expressionPython大数据开发讲义第65页for

循环（一）foriter_variniterable_object:suite_to_repeat语

法• 遍历一个数据集内组员• 在列表解析中使用• 生成器表示式中使用能够明确循环次数StringListTupleDictionaryFileiterable_objectPython大数据开发讲义第66页for

循环（二）字符串就是一个iterable_object• range()返回也是iterable_object>>>foriin

range(3,11,2):print

i,357

9Source>>>s=

'python'>>>forcin

s:print

cpythonPython3.x中print函数用法与Python2.x中语句使用方法也有所改变，比如此处变成print(i,end='

')Python大数据开发讲义第67页猜数字游戏• 程序随机产生一个0~300间整数，玩家竞猜，允许猜屡次，系统给出“猜中”、“太大了”或“太小了”提醒。File#Filename:

guessnum2.pyfromrandomimport

randintx=randint(0,

300)forcountin

range(0,5):print'Pleaseinputanumberbetween

0~300:'digit=

input()ifdigit==x

:print

'Bingo!'elifdigit>

x:print'Toolarge,pleasetryagain.'else:print'Toosmall,pleasetry

again.'Python大数据开发讲义第68页循环中BREAK,CONTINUE和ELSEPython大数据编程Python大数据开发讲义第69页break

语句• break语句终止当前循环，转而执行循环之后语句File#Filename:

breakpro.pysumA=

0i=

1whileTrue:sumA+=

ii+=

1ifsumA>

10:breakprint'i=%d,sum=%d'%(i,sumA)Output:i=6,sumA=15Python大数据开发讲义第70页while

循环和break• 输出2-100之间素数File#Filename:prime.pyfrommathimportsqrtj=2whilej

<=100:i=

2k=sqrt(j)while(i<=k

):ifj%i==0:

breaki

=

i+1if(i

>

k):print

j,j+=

1Output:2357111317

192329313741

434753596167

7173798389

97Python大数据开发讲义第71页for

循环和breakFile• 输出2-100之间素数Output:2357111317

192329313741

434753596167

7173798389

97flag=

1#Filename:prime.pyfrommathimportsqrtforiin

range(2,101):k=

int(sqrt(i))forjinrange(2,k+1):ifi%j==

0:flag=

0breakif( flag ):print

i,Python大数据开发讲义第72页continue

语句在while和for循环中，continue语句作用：– 停顿当前循环，重新进入循环– while循环则判断循环条件是否满足– for循环则判断迭代是否已经结束Python大数据开发讲义第73页continue语句循环中break：File#Filename:

breakpro.pysumA=

0i=

1whilei<=5:sumA+=

iifi==

3:breakprint'i=%d,sum=%d'%

(i,sumA)i+=

1循环中continue：File#Filename:

continuepro.pysumA=

0i=

1whilei<=5:sumA+=

ii+=

1ifi==

3:continueprint'i=%d,sum=%d'%(i,sumA)Python大数据开发讲义第74页猜数字游戏（想停就停，非固定次数）• 程序随机产生一个0~300间整数，玩家竞猜，允许玩家自己控制游戏次数，假如猜中系统给出提醒并退出程序，假如猜错给出“太大了”或“太小了”提醒，假如不想继续玩能够退出。File#Filename:guessnum3.pyfromrandomimportrandintx=randint(0,

300)go=

'y'while(go==

'y'):print'Pleaseinputanumberbetween0~300:'digit=

input()ifdigit==x:print

'Bingo!‘breakelifdigit>

x:print'Toolarge,pleasetryagain.'else:print'Toosmall,pleasetry

again.‘print'Ifyoudonotwanttocontinue,inputn,orinputy.'go=

raw_input()print

goelse:print

'Goodbye!'Python大数据开发讲义第75页循环中else语句• 循环中else：– 假如循环代码从break处终止，跳出循环– 正常结束循环，则执行else中代码>>>

k=5>>>foriin

range(1,10):if

k==3:breakelse:print

i9SourcePython大数据开发讲义第76页自定义函数Python大数据编程Python大数据开发讲义第77页函数函数调用之前必须先定义内置函数自定义函数Python大数据开发讲义第78页语

法自定义函数创建deffunction_name([arguments]):"optionaldocumentationstring"function_suiteSource>>>def

addMe2Me(x):'applyoperation+toargument'return

(x+x)Python大数据开发讲义第79页自定义函数调用• 函数名加上函数运算符，

一对小括号– 括号之间是全部可选参数– 即使没有参数，

小括号也不能省略>>>

addMe2Me(3.7)7.4>>>

addMe2Me(5)10>>>

addMe2Me('Python')'PythonPython'SourceTypeError:addMe2Me()takesexactly1argument(0

given)Source>>>

addMe2Me()Traceback(mostrecentcall

last):File"<pyshell#6>",line1,in<module>addMe2Me()Python大数据开发讲义第80页自定义函数• 输出1-100之间素数if

name

==

"

main

":foriin

range(2,101):if

isprime(i):print

i,File#Filename:prime.pyfrommathimportsqrtdef

isprime(x):ifx==1:return

Falsek=

int(sqrt(x))forjin

range(2,k+1):ifx%j==

0:returnFalsereturn

TrueOutput:2357111317

192329313741

434753596167

7173798389

97与Python2.x中一样，用“if

name

=='main

'”来判断是否是在直接运行该.py文件Python大数据开发讲义第81页默认参数（一）• 函数参数能够有一个默认值，

假如提供有默认值，在函数定义中，默认参数以赋值语句形式提供Source>>>deff(x=

True):'''whetherxisacorrectwordornot'''if

x:print'xisacorrect

word'print

'OK'>>>

f()xisacorrectwordOK>>>

f(False)OKPython大数据开发讲义第82页默认参数（二）• 默认参数值能够改变Source>>>deff(x,y=

True):'''xandybothcorrectwordsornot'''if

y:printx,'andybothcorrect'printx,'is

OK'>>>f

(68)68andyboth

correct68is

OK>>>

f(68,False)68is

OKPython大数据开发讲义第83页默认参数（三）• 默认参数普通需要放置在参数列表最终Sourcedeff(y=True,

x):'''xandybothcorrectwordsornot

'''if

y:printx,'andybothcorrect'printx,'is

O