利用pandas模塊編程處理數(shù)據(jù) 課件 2022-2023學(xué)年浙教版（2019）高中信息技術(shù)必修1

第四章

數(shù)據(jù)處理與應(yīng)用4.2.2

pandas數(shù)據(jù)處理人生苦短我用pythonpandas處理數(shù)據(jù)

數(shù)據(jù)處理可以使用現(xiàn)成的軟件或平臺(tái),也可以通過編寫程序?qū)崿F(xiàn)。Python語(yǔ)言豐富的標(biāo)準(zhǔn)模塊和擴(kuò)展庫(kù)提供了許多高效靈活的函數(shù),可以幫助我們較好地進(jìn)行數(shù)據(jù)整理。numpyscipypandasmatplotlibDateFrame數(shù)據(jù)結(jié)構(gòu)Series數(shù)據(jù)結(jié)構(gòu)在Python中引入pandas模塊：importpandasaspd1.SeriesSeries是一維數(shù)組，由一個(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)。importpandasaspd

#導(dǎo)入pandas模塊s1=pd.Series([3,4,5,6])print(s1)03142536左列：index右列：valuesimportpandasaspd

#導(dǎo)入pandas模塊s2=pd.Series([“高二”,16,180],index=["年級(jí)","年齡","身高"])print(s2)年級(jí)

高二年齡

16身高

180左列：index右列：valuesforiins2.index:print(i)運(yùn)行結(jié)果：年級(jí)年齡身高foriins2.values:print(i)運(yùn)行結(jié)果：高二16180Series數(shù)據(jù)結(jié)構(gòu)

Series數(shù)據(jù)結(jié)構(gòu)

importpandasaspd

#導(dǎo)入pandas模塊s2=pd.Series([“高二”,16,180],index=["年級(jí)","年齡","身高"])#通過索引賦值，改變s2中對(duì)象的值s2[“身高”]=190print(s2)年級(jí)

高二年齡

16身高

190DateFrame數(shù)據(jù)結(jié)構(gòu)DataFrame對(duì)象是一個(gè)二維表格,由1個(gè)索引列（index）和若干個(gè)數(shù)據(jù)列組成。其中，每列中的元素類型必須一致，而不同的列可以擁有不同的元素類型，由長(zhǎng)度相等的列表或字典創(chuàng)建。importpandasaspddata={"name":["王曉明","李靜","田海"],"sex":["男","女","男"],"aged":[20,19,21]}#字典是無序的，因此需要通過columns指定列索引的排列順序df=pd.DataFrame(data,columns=["name","sex","aged"])print(df)indexcolumns中存放列標(biāo)題，決定數(shù)據(jù)列輸出的順序，若columns不設(shè)置參數(shù)，默認(rèn)列順序?yàn)椋簄ame,sex,agedimportpandasaspddata={"name":["王曉明","李靜","田海"],"sex":["男","女","男"],"aged":[20,19,21]}#字典是無序的，因此需要通過columns指定列索引的排列順序df=pd.DataFrame(data,columns=["aged","sex","name"])print(df)DateFrame數(shù)據(jù)結(jié)構(gòu)還可以通過導(dǎo)入二維數(shù)據(jù)文件創(chuàng)建DataFrame對(duì)象pd.read_csv(文件名)從csv文件導(dǎo)入數(shù)據(jù)pd.read_excel(文件名)從excel文件中導(dǎo)入數(shù)據(jù)df.to_csv(文件名)導(dǎo)入數(shù)據(jù)到csv文件df.to_excel(文件名)導(dǎo)入數(shù)據(jù)到excel文件importpandasaspddf=pd.read_excel("test.xlsx")print(df)test.xlsxDateFrame數(shù)據(jù)結(jié)構(gòu)importpandasaspddata={"name":["王曉明","李靜","田海"],"sex":["男","女","男"],"aged":[20,19,21]}df=pd.DataFrame(data,columns=["name","sex","aged"])print(df)foriindf.index:print(i)foriindf.columns:print(i)foriindf.values:print(i)df.T#行列轉(zhuǎn)置運(yùn)行結(jié)果：012運(yùn)行結(jié)果：namesexaged運(yùn)行結(jié)果：[‘王曉明’‘男’

20][‘李靜’‘女’19][‘田?！小?1]DateFrame數(shù)據(jù)結(jié)構(gòu)查看DataFrame中的數(shù)據(jù)列：通過字典記法，屬性檢索importpandasaspddata={"name":["王曉明","李靜","田海"],"sex":["男","女","男"],"aged":[20,19,21]}df=pd.DataFrame(data,columns=["name","sex","aged"])print(df)#通過屬性檢索列print()#通過字典記法檢索列print(df[“sex”])#修改列內(nèi)容df.aged=[20,20,22]print(df)DateFrame數(shù)據(jù)結(jié)構(gòu)查看DataFrame中的數(shù)據(jù)行：通過索引查看指定行、通過布爾型數(shù)據(jù)選取滿足條件的行importpandasaspddata={"name":["王曉明","李靜","田海"],"sex":["男","女","男"],"aged":[20,19,21]}df=pd.DataFrame(data,columns=["name","sex","aged"])print(df)#使用索引查看指定行print(df[0:2])#使用布爾型數(shù)據(jù)選擇行print(df[df[“sex”]==“女”])#使用at[]精準(zhǔn)定位print(df.at[2,”name”])DateFrame數(shù)據(jù)結(jié)構(gòu)DateFrame數(shù)據(jù)結(jié)構(gòu)importpandasaspddata={"name":["王曉明","李靜","田海"],"sex":["男","女","男"],"aged":[20,19,21]}df=pd.DataFrame(data,columns=["name","sex","aged"])print(df)#添加一行數(shù)據(jù)df_add=df.append({"name":"張亮","sex":"男","aged":17},ignore_index=True)print(df_add)#刪除“sex”列df_delc=df.drop(“sex”,axis=1)print(df_delc)#刪除第1行df_delr=df.drop(0)print(df_delr)append()/drop()不改變?cè)袑?duì)象中的數(shù)據(jù)；del會(huì)永久刪除原有數(shù)據(jù)添加一列，并賦值df[“height”]=[175,180,182]DateFrame數(shù)據(jù)結(jié)構(gòu)#groupby()分組#mean()計(jì)算平均值g=df.groupby(“地區(qū)”，as_index=False)print(g.mean())--------------or-----------g=df.groupby(“地區(qū)”，as_index=False).mean()#按價(jià)格降序排序df_sort=df.sort_values(“價(jià)格”，ascending=False)print(df_sort)1.按索引排序sort_index()2.按值排序sort_values()參數(shù)：axis=0(默認(rèn))