中小學(xué)pandas1公開課教案教學(xué)設(shè)計(jì)課件案例測(cè)試練習(xí)卷題

pandas1：入門+SeriesPandas，即pythondataanalysis的組合縮寫，是python語(yǔ)言中基于numpy和matplotlib搭建的第三方數(shù)據(jù)分析庫(kù)，與后兩者共同構(gòu)成了python數(shù)據(jù)分析的基礎(chǔ)工具，享有數(shù)據(jù)分析三劍客之名。pandas導(dǎo)入pandas模塊的方法如下:importpandasaspd#pd是用戶為導(dǎo)入模塊取的別名pandas中的數(shù)據(jù)結(jié)構(gòu)pandas提供了Series和DataFrame兩種數(shù)據(jù)結(jié)構(gòu)。SeriesSeries是一種一維的數(shù)據(jù)結(jié)構(gòu)，包含一個(gè)數(shù)組的數(shù)據(jù)和一個(gè)與數(shù)據(jù)關(guān)聯(lián)的索引(index)，索引值默認(rèn)是從0起遞增的整數(shù)。列表、字典等可以用來(lái)創(chuàng)建Series數(shù)據(jù)結(jié)構(gòu)，與列表不同的是，Series的索引可以指定，類型可以為字符串型。創(chuàng)建Series功能：創(chuàng)建1個(gè)Series結(jié)構(gòu)類型的對(duì)象，存儲(chǔ)3名同學(xué)的身高值。序號(hào)代碼運(yùn)行結(jié)果說(shuō)明例1importpandasaspds1=pd.Series([166,178,180])print(s1)016611782180dtype:int64通過(guò)列表創(chuàng)建索引默認(rèn)例2importpandasaspds2=pd.Series([166,178,180],index=["s01","s02","s03"])print(s2)s01166s02178s03180dtype:int64通過(guò)列表創(chuàng)建索引指定例3importpandasaspds3=pd.Series({'a':1,'b':2,'c':10,'d':20})print(s3)a1b2c10d20dtype:int64通過(guò)字典創(chuàng)建查看Series對(duì)象的屬性值功能：查看Series對(duì)象的index、values屬性值。序號(hào)代碼運(yùn)行結(jié)果說(shuō)明例4importpandasaspds4=pd.Series([166,178,180])foriins4.index:print(i)012查看index例5importpandasaspds5=pd.Series([166,178,180])foriins5.values:print(i)166178180查看values例6importpandasaspds6=pd.Series([166,178,180])foriins6:print(i)166178180與例5結(jié)果相同選取并修改Series對(duì)象中的值功能：通過(guò)索引可以選取Series對(duì)象中的值,通過(guò)賦值語(yǔ)句可以修改Series對(duì)象中的值。序號(hào)代碼運(yùn)行結(jié)果說(shuō)明例7importpandasaspds7=pd.Series([166,178,180])s7[0]=168print(s7)016811782180dtype:int64修改索引為0的數(shù)據(jù)為168例8importpandasaspds8=pd.Series([166,178,180],index=["s01","s02","s03"])s8["s01"]=168print(s8)s01168s02178s03180dtype:int64修改索引為“s01”的數(shù)據(jù)為168例9importpandasaspds9=pd.Series({'a':1,'b':2,'c':10,'d':20})s9['c']=123print(s9)a1b2c123d20dtype:int64修改索引為‘c’的數(shù)據(jù)為123課中例析題：1.在python中為了對(duì)數(shù)據(jù)進(jìn)行分析整理,可以使用的庫(kù)是（）A.requests庫(kù) B.pandas庫(kù) C.matplotlib庫(kù) D.jieba庫(kù)2.Pandas中Series左列默認(rèn)index是從()開始生成。A.0 B.1 C.NaN D.a3.代碼創(chuàng)建Series對(duì)象,第二行數(shù)據(jù)的下標(biāo)是（）importpandasaspds1=pd.Series([188,167,156],index=["I.","II.","III."])A.167 B.2. C.I. D.II.4.下列有關(guān)Series說(shuō)法錯(cuò)誤的是（）A.Series中index和values長(zhǎng)度必須一致B.Series中必須指定indexC.兩個(gè)index不同的Series可以相加D.Series經(jīng)過(guò)計(jì)算后的index順序和計(jì)算前不一定一致5.Pandas中有兩個(gè)重要的數(shù)據(jù)結(jié)構(gòu):___________、____________pandas2：DataFrameDataFrameDataFrame是一種二維的數(shù)據(jù)結(jié)構(gòu)，由1個(gè)索引列(index)和若干個(gè)數(shù)據(jù)列組成，每個(gè)數(shù)據(jù)列可以是不同的類型?！綝ataFrame是由一組數(shù)據(jù)與一對(duì)索引（行索引和列索引）組成的表格型數(shù)據(jù)結(jié)構(gòu)。其存儲(chǔ)形式與Excel的數(shù)據(jù)存儲(chǔ)形式很相近。】DataFrame可以看作是共享同一個(gè)index的Series的集合。創(chuàng)建DataFrame對(duì)象的方法很多，通常用一個(gè)相等長(zhǎng)度的列表或字典來(lái)創(chuàng)建。創(chuàng)建DataFrame使用函數(shù).DataFrame()創(chuàng)建DataFrame對(duì)象功能：使用函數(shù).DataFrame()創(chuàng)建DataFrame對(duì)象序號(hào)代碼運(yùn)行結(jié)果說(shuō)明例1importpandasaspddata=["a","b","c","d"]df1=pd.DataFrame(data)print(df1)00a1b2c3d通過(guò)列表創(chuàng)建，創(chuàng)建一列數(shù)據(jù)例2importpandasaspddata=[["a","A"],["b","B"],["c","C"],["d","D"]]df2=pd.DataFrame(data)print(df2)010aA1bB2cC3dD通過(guò)列表創(chuàng)建，創(chuàng)建兩列數(shù)據(jù)例3importpandasaspddata={"小寫":["a","b","c","d"],"大寫":["A","B","C","D"]}df3=pd.DataFrame(data)print(df3)小寫大寫0aA1bB2cC3dD通過(guò)字典創(chuàng)建，創(chuàng)建兩列數(shù)據(jù)例4importpandasaspddata={"小寫":["a","b","c","d"],"大寫":["A","B","C","D"]}df4=pd.DataFrame(data,index=["甲","乙","丙","丁"])print(df4)小寫大寫甲aA乙bB丙cC丁dD通過(guò)字典創(chuàng)建，字典中的key值相當(dāng)于列索引，如要加入行索引，可使用index=[]參數(shù)例5importpandasaspddata={"小寫":["a","b","c","d"],"大寫":["A","B","C","D"]}df5=pd.DataFrame(data,columns=["大寫","小寫"])print(df5)大寫小寫0Aa1Bb2Cc3Ddcolumns可以設(shè)定數(shù)據(jù)列的順序直接讀取數(shù)據(jù)文件創(chuàng)建DataFrame對(duì)象功能：直接讀取二維數(shù)據(jù)文件創(chuàng)建DataFrame對(duì)象①read_excel()可以從.xlsx文件導(dǎo)入生成DataFrame②read_csv()可以從.csv文件導(dǎo)入生成DataFrame序號(hào)代碼運(yùn)行結(jié)果說(shuō)明例6importpandasaspddf6=pd.read_excel("test.xlsx")print(df6)namesexage0Helenfemale151Brucemale192Jackmale17導(dǎo)入.xlsx文件創(chuàng)建DataFrame對(duì)象。(以下是test.xlsx中數(shù)據(jù))例7importpandasaspddf7=pd.read_csv("test.csv")print(df7)namesexage0Helenfemale151Brucemale192Jackmale17導(dǎo)入.csv文件創(chuàng)建DataFrame對(duì)象。(以下是test.csv中數(shù)據(jù))name,sex,ageHelen,female,15Bruce,male,19Jack,male,17查看DataFrame對(duì)象的屬性值功能：查看Series對(duì)象的行索引index、列標(biāo)題columns、值values，并行列轉(zhuǎn)置。序號(hào)代碼運(yùn)行結(jié)果說(shuō)明例8importpandasaspddf8=pd.read_excel("test.xlsx")foriindf8.index:print(i)012行索引index(以下是test.xlsx中數(shù)據(jù))例9importpandasaspddf9=pd.read_excel("test.xlsx")foriindf9.columns:print(i)namesexage①列標(biāo)題columns②df9.columns可簡(jiǎn)寫為df9例10importpandasaspddf10=pd.read_excel("test.xlsx")foriindf10.values:print(i)['Helen''female'15]['Bruce''male'19]['Jack''male'17]值values例11importpandasaspddf11=pd.read_excel("test.xlsx")print(df11.T)012nameHelenBruceJacksexfemalemalemaleage151917行列轉(zhuǎn)置選取并修改DataFrame對(duì)象中的值選取DataFrame對(duì)象中的值功能：DataFrame對(duì)象中的一列可以通過(guò)字典記法或?qū)傩詠?lái)檢索，可以通過(guò)布爾型數(shù)據(jù)選取滿足條件的行，使用at[]方法可以根據(jù)行標(biāo)簽和列標(biāo)簽選取單個(gè)值。選取方法1：法字典記法選取方法2：屬性選取方法3：布爾型數(shù)據(jù)選取方法4：at[]方法序號(hào)代碼運(yùn)行結(jié)果說(shuō)明例12importpandasaspddf12=pd.read_excel("test.xlsx")print()0Helen1Bruce2JackName:name,dtype:object字典記法。(以下是test.xlsx中數(shù)據(jù))例13importpandasaspddf13=pd.read_excel("test.xlsx")print(df13["sex"])0female1male2maleName:sex,dtype:object屬性。例14importpandasaspddf14=pd.read_excel("test.xlsx")print(df14[df14["sex"]=="male"])namesexage1Brucemale192Jackmale17布爾型數(shù)據(jù)。選取sex為male的數(shù)據(jù)。例15importpandasaspddf15=pd.read_excel("test.xlsx")print(df15.at[2,"name"])Jackat[]方法。選取第3行”name”列數(shù)據(jù)。通過(guò)賦值修改DataFrame對(duì)象中的值功能：DataFrame對(duì)象中的列可以通過(guò)賦值來(lái)修改序號(hào)代碼運(yùn)行結(jié)果說(shuō)明例16importpandasaspddf16=pd.read_excel("test.xlsx")df16.age=[16,20,18]print(df16)namesexage0Helenfemale161Brucemale202Jackmale18(以下是test.xlsx中數(shù)據(jù))將DataFrame結(jié)果導(dǎo)出功能：DataFrame對(duì)象導(dǎo)出為.xlsx文件可使用函數(shù)to_excel()函數(shù)DataFrame對(duì)象導(dǎo)出為.csv文件可使用函數(shù)to_csv()函數(shù)序號(hào)代碼運(yùn)行結(jié)果說(shuō)明例17importpandasaspddata=[["a","A"],["b","B"],["c","C"],["d","D"]]df17=pd.DataFrame(data)df17.to_excel("df2excel.xlsx")df17.to_csv("df2csv.csv")生成兩個(gè)文件：DataFrame結(jié)果導(dǎo)出在文件中課中例析題：1.在pandas庫(kù)中用于查看列類型的函數(shù)是A.pandas.DataFrame.shapeB.pandas.DataFrame.dtypesC.pandas.DataFrame.columnsD.pandas.DataFrame.columns.dtypes2.使用to_excel()函數(shù),讀取Excel文件創(chuàng)建DataFrame對(duì)象,也可以使用read_excel()函數(shù),創(chuàng)建Excel文件保存數(shù)據(jù)。_____________(填:對(duì)或錯(cuò))3.請(qǐng)使用at[]方法,獲取一個(gè)df對(duì)象中第6行,“姓名”列數(shù)據(jù)：_____________4.以下程序用data建立一個(gè)DataFrame,先后取出member和class數(shù)據(jù),并轉(zhuǎn)置顯示。______________________________________(1)__data={'grade':['Grade1','Grade1','Grade1','Grade2','Grade2'],'class':['Class1','Class2','Class3','Class1','Class2'],'member':[43,45,44,46,47]}_____________________________________(2)___print(df.T)課后鞏固題：1.下列哪個(gè)不是Series對(duì)象的屬性（）A.indexB.valuesC.nameD.T2.下列關(guān)于DataFrame的說(shuō)法,錯(cuò)誤的是()A.可以讀取Excel文件創(chuàng)建DataFrame對(duì)象B.創(chuàng)建DataFrame對(duì)象后,可以修改索引C.可以將Series轉(zhuǎn)換為DataFrameD.Series和DataFrame之間不能進(jìn)行運(yùn)算3.有如下Python程序段:importpandasaspds1=pd.Series([120,130,180],index=["s01","s02","s03"])s1[1]=125print(s1[s1<179])執(zhí)行代碼后,輸出的結(jié)果是（）A.120125180B.s01120s02125C.s01125s02130D.1201254.有如下Python程序段:importpandasaspddata=[[1,2,3],[3,4,5],[6,7,8]]df1=pd.DataFrame(data,index=["a","b","c"],columns=["e","f","g"])print(df1)輸出的結(jié)果是（）A.efg123345678B.efga123b345c678C.a123b345c678D.abce123f345g6785.Series和DataFrame是pandas模塊中的數(shù)據(jù)結(jié)構(gòu),下列描述錯(cuò)誤的是（）A.DataFrame