《Python數(shù)據(jù)分析與應(yīng)用》實驗二 pandas統(tǒng)計分析基礎(chǔ)

實驗二Pandas統(tǒng)計分析基礎(chǔ)任務(wù)1讀取mtcar數(shù)據(jù)并實現(xiàn)以下操作（1）查看mtcar數(shù)據(jù)集的元素的個數(shù)、維度、大小等信息，輸出表的列名。importpandasaspdmtcars=pd.read_csv（'D:\\桌面\\實驗二\\data\\mtcars.csv'）print（'mtcars的元素個數(shù)為：\n',mtcars.size）print（'mtcars的元素維度為：\n',mtcars.ndim）print（'mtcars的元素大小為：\n',mtcars.shape）print（'mtcars的表的列名為：\n',mtcars.columns）In[1]:runfile（JCr/Users/Ad/nin/.spyder-py3/temp-pyJjwdir='C:/Users/Admin/.spyder-py3r）mtcars的元素個數(shù)為：3S4mtcars的元素維度為：2mtcars的元素大小為：（32,12）mtcars的表的列名為：Index（['Unnamed:&'±'mpg'±'cyl1±'disp't"hp"'drat'tRwt't'qsecp±'vs1±Fam'±'gear't'carb'dtype=,object'）（2）使用describe方法對整個mtcar數(shù)據(jù)集進(jìn)行描述性統(tǒng)計。importpandasaspdmtcars=pd.read_csv（'D:\\桌面\\實驗二\\data\\mtcars.csv'）print（'對整個mtcar數(shù)據(jù)集進(jìn)行描述性統(tǒng)計為：\n',mtcars.describe（））In[2]:runfile（JC:/Users/Adrnin/.spyder-py3/temp-pyJjwdir=對整個mtca『數(shù)據(jù)集進(jìn)行描述性統(tǒng)計為：mpg cyl disp amcount … 32.mean 2&.090625 6.1875&& 23&.721S75 ... 0.4^625^ 3.std 6.926948 1.785922 123.938694 ... 0.498991 0.min 10.4^090 4,明豹的 71.100&&9 ... 。，麗麗麗 3.25% L5.425&&9 4.0000S& 120.B25&&9 ... 0.&0000& 3.5&% 19.200&S0 6.0000&S 196.300&&0 ... 0.&0000& 4.75% 22.800S&0 8.0000S& B26.000&&9 ... 1.&0000& 4.max 33.9&0&S0 8.0&00&S 472.陽如陽 ... 1.&& 5.JC:/Users/Admin/.spyder-py3J）gear carb.687500 2.&125.737804 1,6152.&0000& 2.&S00.&0000& 8.&S00[8rowsx11columns]（3）計算不同cyl（氣缸數(shù)）、carb（化油器）對應(yīng)的mpg（油耗）和hp（馬力）的均值。importpandasaspdmtcars=pd.read_csv（'D:\\桌面\\實驗二\\data\\mtcars.csv'）x=mtcars.loc[:,['cyl','carb','mpg','hp']]y=x.groupby（['cyl','carb']）.mean（）print（'不同cyl（氣缸數(shù)）、carb（化油器）對應(yīng)的mpg（油耗）和hp（馬力）的均值為：\n',y）(4)輸出mpg和hp前5個元素。importpandasaspdmtcars=pd.read_csv('D:\\桌面\\實驗二\\data\\mtcars.csv')x=mtcars[['mpg','hp']][:5]print('mpg和hp前5個元素為:\n',x)In[4]:runfile(JCr/Users/Adff^in/.spyder-py3/t^mp.py，丁wdir=JC:/Users/Admin/?spyder-py3J)mpg^flhp前5個無素為:：mpghp021.011021.011022.8 9321.411018/7175(5)輸出mtcar數(shù)據(jù)的前3行元素。importpandasaspdmtcars=pd.read_csv('D:\\桌面\\實驗二\\data\\mtcars.csv')x=mtcars[:][1:3]print('mtcar數(shù)據(jù)的前3行元素為:\n',x)In[5]:runfilef1C:/Users/Admin/.spyder-py3/temp.py'twdir='C:/Users/Admin/.spyder-py31)mtcar教據(jù)的前3行元素為：Unnamed:&mpgcyldisphpdrati/tqsecvsamgearcarbMazdaRX4 Nag： 21.& 6 160,6 11.^ 3.9& 2.875 17.02 & 1 4 4Datsun 716- 22.8 4 108.& 93 3.85 2.32& IS.61 1 1 4 1(6)使用head()和tail()方法輸出前后5行數(shù)據(jù)。importpandasaspdmtcars=pd.read_csv('D:\\桌面\\實驗二\\data\\mtcars.csv')print('mtcar數(shù)據(jù)的前5行元素為:\n',mtcars.head())print('mtcar數(shù)據(jù)的后5行元素為:\n',mtcars.tail())

In[6]:runfile（JC;/Users/Admin/.spyder-py3/tempratEP數(shù)據(jù)的前5行元表為:Unnamed:0mpgcyldisphp…0 MazdaRX4 21.& 6 160.0 110 ■—MazdaRX4Uag 21,0 6 160.0 110Datsun710 22.S 4 108.0 93 …Hornet4Drive 21,4 6 258.? 11? .…HornetSportabout18,7 S360.0175.….pyJjwdir=rC;/Users/Admin/.spyder-py3J）■qsecvsamgearcarb16.46 ? 1 4 417.02 0 1 4 418.61 1 1 4 119.44 1 0 3 117.O2 0 0 3 2[5rowsx12columns]rntcar數(shù)據(jù)的后5行元素為：Unnamed:&mpgLotusEuropaB&..4FordPanteraL15.8Ferrari Dino 19.73& Maserati Bora 15.031 Vo1\ao 142E 21?4守]48684Ldisphp■■,95.1113…351,0264…145.0175…301.0335…121-0109…qsecvs16,9 1?0018-6 1ain11111gear55554carb24682[5rowsx12columns](7)用loc和iloc分別提取第1列和第3列的數(shù)據(jù)。importpandasaspdmtcars=pd.read_csv（'D:\\桌面\\實驗二\\data\\mtcars.csv'）print（'mtcar數(shù)據(jù)的第1列和第3列的數(shù)據(jù)為:\n',mtcars.loc[:,['mpg','disp']]）print（'mtcar數(shù)據(jù)的第1列和第3列的數(shù)據(jù)為:\n',mtcars.iloc[:,[1,3]]）In[7]:runfile（己spyd巴己ffip. ^dir='C:/Users/Admin/,spy-der-py3J）Mb『教據(jù)的第1列和第3列的數(shù)據(jù)為：mpgdisp021.0160.0121.0160.0222-8108.0321.4258.0418/7360.0518,1225.0614.3360.0724.4146/7S22,8140,8919.2167.61017.8167.61116.4275,81217.3275,81315.2275,81410.4472.01510.4460.01614/7440.01732.478/71830.475?71933,971,12021.5120,12115.5318.02215.2304.0

2313.3350.0踏19.24^0.025-27.379.02626,0LZ0.32795..115-S351西15.7145..G30-15.03&1.03121.4121.6Me『數(shù)據(jù)的第］列和第M列的數(shù)據(jù)為：di^p021.0160.6121.0160.0222.816-8.6321.425S..04-IE-7360.05IB-1Z258514.3360.0724.4146.7822,8140.B919.2167.6L017.0167.61116.4275-81Z17.3275-81315.2275-8L41*0.4472.01516.4LG14,744?.&L732.47S.7IS3G.475-7L933.971.12921.5120.12115.5318-02215.2304-02313.3350-02419.22527.379.02626.0120.3273&.495-12S15,S351.02919,7145.&15,&m眥.83121.4121.&取出列名為mpg、hp，行名為2,3,4的數(shù)據(jù)。importpandasaspdmtcars=pd.read_csv('D:\\桌面\\實驗二\\data\\mtcars.csv')print('列名為mpg、hp,行名為2,3,4的數(shù)據(jù)為:\n',mtcars.loc[2:4,['mpg','hp']])In[8]srunfile(JC:/Users/ZIdmin/.spyder-py3/temp-pyJwdir=JC;/Users/Admin/.spyder-py3r)列名為叩目、hp，行名為W，3,4的數(shù)據(jù)為'：mpghp22.8 9321.411018.7175取出列位置為2和4,行位置為5,6,7的數(shù)據(jù)。importpandasaspdmtcars=pd.read_csv('D:\\桌面\\實驗二\\data\\mtcars.csv')print（'列位置為2和4,行位置為5,6,7的數(shù)據(jù)為:\n',mtcars.iloc[5:8,[2,4]]）#前閉后開區(qū)間In[9]srunfilefJC:/Users/Admin/.spyder-py3/temp-pyrjwdir=1C:/Users/Admin/.spyd^r-py3J)列位-置-為w和4,行位置為5，E，7的數(shù)據(jù)為：cylhpTOC\o"1-5"\h\z6 1058 2454 62取出列位置為3,行名為2-6的數(shù)據(jù)。importpandasaspdmtcars=pd.read_csv('D:\\桌面\\實驗二\\data\\mtcars.csv')print('列位置為3，行名為2-6的數(shù)據(jù)為:\n',mtcars.loc[2:6,['disp']])（11）新增1列，名稱為abc（abc=mpg+hp），輸出前5行數(shù)據(jù)。importpandasaspdmtcars=pd.read_csv('D:\\桌面\\實驗二\\data\\mtcars.csv')mtcars['abc']=mtcars['mpg']+mtcars['hp']print('輸出前5行數(shù)據(jù)為:\n',mtcars.head())In[11]:runfile(JCr/Users/Admin/.spyd&r-py3/temp-pyJj輸出前5-行數(shù)據(jù)為：Unnamed:&mpgcyldisphp-...vs& MazdaRX4 21.& 6 16&.9 11.? ... 0MazdaRX4Uag 21.0 6 16&.0 11.? ... 0Datsun710 22.8 4 108.9 93 ... 1Hornet4Drive 21.4 6 258.0 11.0 ... 1HornetSportaboutIS.7 836&.9175 ... 0wdir=JCr/Users/Admin/.spyd&r-py3x)amgearcarb abc14 4131.014 4131.01 4 1115.8031131.40 3 2193/7[5rowsk13columns](12)刪除前兩行數(shù)據(jù)。importpandasaspdmtcars=pd.read_csv('D:\\桌面\\實驗二\\data\\mtcars.csv')print(刪除前兩行數(shù)據(jù)前的長度為:\n',len(mtcars))mtcars.drop(labels=range(0,2),axis=0,inplace=True)print(刪除前兩行數(shù)據(jù)后的長度為:\n',len(mtcars))print('前五行數(shù)據(jù)為：\n',mtcars.head())In[12]:runfile(rC:/Users/.spyder-py3/temp-pyfjwdir=JC:/Users/Admin/.spyder-pySJ)刪除前兩行數(shù)據(jù)前的長度為:32刪除前兩行數(shù)據(jù)后的長度為=3?前五行數(shù)據(jù)為：Unnamed:?mpgcyldisphp qsecvsamgearcarbDatsun71022.8 4108.0 93…18,61 11 4 1Hornet4Drive21.4 6258.011? 19.44 1 0 3 1HornetSportabout 18/7 8 360.0 175 ■…17^02 0 0 3 2Valiant 18,1 6 225.0 105 20.22 1 0 3 1Duster36014.3 8360.0245■…15.84 0 0 3 4[5rowsx12columns](13)刪除abc列。importpandasaspdmtcars=pd.read_csv('D:\\桌面\\實驗二\\data\\mtcars.csv')mtcars['abc']=mtcars['mpg']+mtcars['hp']print(刪除abc前列索引為:\n',mtcars.columns)mtcars.drop(labels='abc',axis=1,inplace=True)print(刪除abc后列索引為:\n',mtcars.columns)In[13]:runfile(JC:/Users/Adniin/.spyder-py3/temp-pyJjwdir=JCr/Users/Admin/.spyder-py3刪蹤ahc前列索引為：Index(['Unnamed::&'t'mpg't'cyl11Fdisp't'hp"'drat11"wt"'qsec't1vs'±"am'±'gear't'carb'±'abc']tdtype='object')刪除北匚后列索引為二Index(['Unnaimed:j'mpg"j"cyl"j'disp"j'hp'』"drat"j'wtvj"qsecBj"vsFj'am'\pgear"j'carb"]jdtype=Fobject1)任務(wù)2導(dǎo)入鳶尾花數(shù)據(jù)，實現(xiàn)以下操作特別注意：導(dǎo)入后的DataFrame僅含5列：Sepal.Length,Sepal.Width,Petal.Length,Petal.Width,Species讀入數(shù)據(jù)，輸出前五行importpandasaspdiris=pd.read_csv('D:\\^W\\實驗二\\data\\iris.csv')print('前五行的數(shù)據(jù)為:\n',iris.head())In[14]srunfilefJC:/Users/Admin/.spyder-py3/temp-pyJjwdir=JC;/Users/Admin/.spyder-py3r)前五行的數(shù)據(jù)為：Unnamed!9Sepal.LengthSepal.WidthPetal.LengthPetal.WidthSpecies015.13.5L40.2setosa124.93,01.40.2setosa234/73.21-3@.2setosa344.63,1L50.2setosa455.03.61.40.2setosa統(tǒng)計每個品種的數(shù)量importpandasaspdiris=pd.read_csv('D:\\^W\\實驗二\\data\\iris.csv')print('每個品種的數(shù)量為:\n',iris['Species'].value_counts())In[15]srunfilefJCr/Users/Admin/.spyder-py3/temp.pyJjwdir=JC:/Users/4dmin/.spyc/&r-py3J)每個品制的數(shù)里為：versicolor5?virginica 50setosa 50Nlame:SpecieSjdtype:int64按品種劃分，每個品種的花萼，花瓣的長度和寬度的最大值分別是多少?importpandasaspdiris=pd.read_csv('D:\\桌面\\實驗二\\data\\iris.csv')print('每個品種的數(shù)量為:\n',iris.groupby('Species').max())In[16]srunfile(rCr/Users/Admin/.spyder-py3/temp.pjzJjwdir=JC:/Users/Admin/.^pyder-py3J)每個品種的數(shù)里為：Unnamed:0SpeciesSepal.LengthSepal.WidthPetal.LengthPetal.Widthsetosa505.84.4L99.6versicolor1007.03.45.11,8virginica1507.93.86.92.5計算每個品種所有屬性(花瓣、花萼的長度和寬度)數(shù)值的跨度范圍，即最大值減去最小值importpandasaspdiris=pd.read_csv('D:\\^W\\實驗二\\data\\iris.csv')defrange_iris(arr):returnarr.max()-arr.min()print('每個品種所有屬性數(shù)值的跨度范圍為:\n',iris.groupby('Species').agg(range_iris))In[17]:runfile('I:/UEJLm/AdtTti』/.務(wù)pyd巴尸一pyM/t巴用p?py'」wdir= /Users/Admin/.spyder-py3r)每個品種所有屬性數(shù)值的跨度范圍為：Unnamed:&Sepal.LengthSepal.WidthPetal.L-engthPetal.WidthSpeciessetosa491.52.1磯90.5versicolor492.11,42.10,8virginica493.01.62.41,1計算每個品種所有屬性(花瓣、花萼的長度和寬度)數(shù)值的平均值、最大值importpandasaspdiris=pd.read_csv('D:\\^W\\實驗二\\data\\iris.csv')print('每個品種所有屬性數(shù)值的平均值、最大值為:\n',iris.groupby('Species').mean(),iris.groupby('Species').max())

In[18]:runfile（xC:/Users/Admin/,spyder-py3/te^p\pyTjwdir=JC;/Users/Admin/.spyder-py3每個品種所有屬性數(shù)值的平均值、最大值為：Sepal.WidthPetal.LengthPetal.WidthUnnamed:?Sepal,LengthSpeciessetosa25.55.0063.4281.4629.246versicolor75.55,9362.7704.2601.326virginica125.56.5882.9745.5522.926Unnamed:&Sepal,LengthSepal.WidthPetal.LengthPetal.WidthSpeciessetosa5?5.84.41.90.6versicolor133.45..11.8virginica1507.93.86..92.5任務(wù)3從excel文件foods.xlsx讀取數(shù)據(jù)，并將ID列作為索引。（1）輸出該DataFrame。importpandasaspdfoods=pd.read_excel（'D:\\桌面\\實驗二\\data\\foods.xlsx'）print（'輸出為:\n',foods）In[19]:runfilefJC:/Users/Adjnin/.spyder-py3/temp■PPJjwdir=JC:/Users/Admin/.spyder-py3J）輸出為"ID產(chǎn)地伯格數(shù)里水果類別0 1 美國 5 5蘋果水:果2 中國 5 5 梨水果3 中國盟 9草莓水果4 中國 3 3番茄蔬菜5新西蘭 3W黃瓜蔬菜6新西蘭13 19羊肉肉類E 7 美國20 8牛肉肉類（2）生成如下樣式的透視表，統(tǒng)計不同產(chǎn)地和類別食品價格及數(shù)量的平均值。價格 數(shù)量類別水果肉婁蔬菜水果肉璽蔬菜產(chǎn)地中國7.5NaN3.07.0NaNS.O新西蘭NaM1303.0NaN10.02.0美國5.020.0NaN5.08.0NaNimportpandasaspdfoods=pd.read_excel（'D:\\桌面\\實驗二\\data\\foods.xlsx'）print（'透視表為:\n',foods.pivot_table（[價格','數(shù)量'],index='產(chǎn)地',columns='類別'））In[20]:runfilefJC;/Us^rs/Admin/.spyder-py3/temp-pyJjwdir=JCr/Users/Admin/.spyder-py3J）透祈表為：價格 數(shù)里類別水果肉類蔬菜水果肉類蔬菜中國7.5NaN3.07.0PlaPI3.0新西蘭 13.03.0HaPI10.92.9美國5.02&.&HaPI 8.0-HaPI

生成如下樣式的透視表，統(tǒng)計不同產(chǎn)地和類別食品價格的最大值，