How to Read Specific Columns from CSV File in Python - Finxter

Method 4: Using csv Module · Import the csv module and open up the csv file. · Allow Python to read the csv file as a dictionary using csv. · Once the file has ... Skiptocontent Menu 5/5-(1vote) TableofContents Method1:UsingPandas➤List-BasedIndexingofaDataFrameMethod2:IntegerBasedIndexingwithilocMethod3:Name-BasedIndexingwithloc()Method4:UsingcsvModuleConclusionLearnPandastheFunWaybySolvingCodePuzzles AQuickGlanceatTheSolutions[Eachsolutionstaysfor5-10secs.] Problem:GivenaCSVfile,howtoreadonlyspecificcolumn(s)fromthecsvfile?(Readingaspecificcolumnfromacsvfilewillyieldalltherowvaluespertainingtothatcolumn.) Example:Consierthefollowingcsvfile(countries.csv): Country,Capital,Population,Area Germany,Berlin,"84,267,549","348,560" France,Paris,"65,534,239","547,557" Spain,Madrid,"46,787,468","498,800" Italy,Rome,"60,301,346","294,140" India,Delhi,"1,404,495,187","2,973,190" USA,Washington,"334,506,463","9,147,420" China,Beijing,"1,449,357,022","9,388,211" Poland,Warsaw,"37,771,789","306,230" Russia,Moscow,"146,047,418","16,376,870" England,London,"68,529,747","241,930" Question:Howwillyoureadtheabovecsvfileanddisplaythefollowingcolumns– CountrycolumnalongwiththeCapitalcolumn?Allvaluesinthepopulationcolumn? Method1:UsingPandas UsingthePandaslibraryisprobablythebestoptionifyouaredealingwithcsvfiles.Youcaneasilyreadacsvfileandstoreanentirecolumnwithinavariable. Code: importpandasaspd df=pd.read_csv("countries.csv") country=df['Country'] #or #country=df.Country capital=df['Capital'] #or #capital=df.Capital #displayingselectedcolumns(CountryandCapital) forx,yinzip(country,capital): print(f"{x}{y}") #displayingasinglecolumn(Country) print() print(df['Population']) Output: GermanyBerlin FranceParis SpainMadrid ItalyRome IndiaDelhi USAWashington ChinaBeijing PolandWarsaw RussiaMoscow EnglandLondon 084,267,549 165,534,239 246,787,468 360,301,346 41,404,495,187 5334,506,463 61,449,357,022 737,771,789 8146,047,418 968,529,747 Name:Population,dtype:object Explanation: Readthecsvfileusingpd.read_csv()Pandasfunction.SavealltheinformationofthecolumnsCountryandCapitalwithinindependentvariablesusing–country=df['Country']Alternatively,youcanalsousecountry=df.Countrycapital=df['Capital']Alternatively,youcanalsousecapital=df.Capital Todisplaythecountrynamesandtheircapitalssimultaneously,youcanbindthetwocolumns,countryandcapital,usingthezip()functionandthendisplayeachcountryalongwithitscapitalusingaforloopuponthezippedobject.Todisplayallthevaluesinthepopulationcolumn,youcansimplyusedf['Population']. TRIVIAzip() isabuilt-infunctioninPythonthattakesanarbitrarynumberof iterables andbindsthemintoasingleiterable,azipobject.Itcombinesthe n-th valueofeachiterableargumentintoatuple.Readmoreaboutzip()here. ➤List-BasedIndexingofaDataFrame Incaseyouarenotcomfortablewithusingzip()todisplaymultiplecolumnsatonce,youhaveanotheroption.Youcansimplyuselist-basedindexingtoaccomplishyourgoal. List-basedindexingisatechniquethatallowsyoutopassmultiplecolumnnamesasa list withinthesquare-bracketselector. Example: importpandasaspd df=pd.read_csv("countries.csv") print() print(df[['Country','Capital']]) Output: CountryCapital 0GermanyBerlin 1FranceParis 2SpainMadrid 3ItalyRome 4IndiaDelhi 5USAWashington 6ChinaBeijing 7PolandWarsaw 8RussiaMoscow 9EnglandLondon Method2:IntegerBasedIndexingwithiloc Approach:Theideahereistousethe df.iloc[rows,columns].values toaccessindividualcolumnsfromtheDataFrameusingindexing.Notethatthefirstcolumnalwayshastheindex0,whilethesecondcolumnhasindex1,andsoon. rowsisusedtoselectindividualrows.Usethe slicingcolon: toensureallrowshavebeenselected.columnsisusedtoselectindividualcolumns.Usecountry=data.iloc[:,0].valuestosavethevaluesoftheCountrycolumn.capital=data.iloc[:,1].valuestosavethevaluesoftheCapitalcolumn.population=data.iloc[:,2].valuestosavethevaluesofthePopulationcolumn. importpandasaspd data=pd.read_csv('countries.csv') country=data.iloc[:,0].values capital=data.iloc[:,1].values population=data.iloc[:,2].values #displayingselectedcolumns print(data[['Country','Capital']]) print() #displayingasinglecolumn(Population) print(population) Output: CountryCapital 0GermanyBerlin 1FranceParis 2SpainMadrid 3ItalyRome 4IndiaDelhi 5USAWashington 6ChinaBeijing 7PolandWarsaw 8RussiaMoscow 9EnglandLondon ['84,267,549''65,534,239''46,787,468''60,301,346''1,404,495,187' '334,506,463''1,449,357,022''37,771,789''146,047,418''68,529,747'] Method3:Name-BasedIndexingwithloc() Insteadofselectingthecolumnsbytheirindex,youcanalsoselectthembytheirnameusingthedf.loc[]selecter. ThefollowingexampleshowshowtoselectthecolumnsCountryandCapitalfromthegivenDataFrame. importpandasaspd data=pd.read_csv('countries.csv') val=data.loc[:,['Country','Capital']] print(val) Output: CountryCapital 0GermanyBerlin 1FranceParis 2SpainMadrid 3ItalyRome 4IndiaDelhi 5USAWashington 6ChinaBeijing 7PolandWarsaw 8RussiaMoscow 9EnglandLondon RelatedTutorial:SlicingDatafromaPandas DataFrameusing.locand.iloc Method4:UsingcsvModule csvmoduleisyetanotherspectacularoptioninPythonthatallowsyoutoplaywithcsvfiles.Letushavealookatthecodethathelpsustoreadthegivencsvfileandthenreadspecificcolumnsfromit: importcsv population=[] withopen('countries.csv',newline='',encoding='utf-8-sig')ascsvfile: data=csv.DictReader(csvfile) forrindata: print("Country",":","Capital") #appendvaluesfrompopulationcolumntopopulationlist population.append(r['Population']) #displayingspecificcolumns(CountryandCapital) print(r['Country'],":",r['Capital']) #displaythepopulationlist print(population) Output: Country:Capital Germany:Berlin Country:Capital France:Paris Country:Capital Spain:Madrid Country:Capital Italy:Rome Country:Capital India:Delhi Country:Capital USA:Washington Country:Capital China:Beijing Country:Capital Poland:Warsaw Country:Capital Russia:Moscow Country:Capital England:London ['84,267,549','65,534,239','46,787,468','60,301,346','1,404,495,187','334,506,463','1,449,357,022','37,771,789','146,047,418','68,529,747'] Explanation: Importthecsvmoduleandopenupthecsvfile.Ensurethatyoufeedintheencodingargumentasithelpstoeliminateanyunreadablecharactersthatmayoccurinthegivencsvfile.withopen('countries.csv',newline='',encoding='utf-8-sig')ascsvfileAllowPythontoreadthecsvfileasadictionaryusingcsv.Dictreaderobject.Oncethefilehasbeenreadintheformofadictionary,youcaneasilyfetchthevaluesfromrespectivecolumnsbyusingthekeyswithinsquarebracketnotationfromthedictionary.Hereeachcolumnrepresentsthekeywithinthegivendictionary. Bonus:Here’saquicklookathowtheDictReader()classlookslike: importcsv population=[] withopen('countries.csv',newline='',encoding='utf-8-sig')ascsvfile: data=csv.DictReader(csvfile) forrowindata: print(row) Output: {'Country':'Germany','Capital':'Berlin','Population':'84,267,549','Area':'348,560'} {'Country':'France','Capital':'Paris','Population':'65,534,239','Area':'547,557'} {'Country':'Spain','Capital':'Madrid','Population':'46,787,468','Area':'498,800'} {'Country':'Italy','Capital':'Rome','Population':'60,301,346','Area':'294,140'} {'Country':'India','Capital':'Delhi','Population':'1,404,495,187','Area':'2,973,190'} {'Country':'USA','Capital':'Washington','Population':'334,506,463','Area':'9,147,420'} {'Country':'China','Capital':'Beijing','Population':'1,449,357,022','Area':'9,388,211'} {'Country':'Poland','Capital':'Warsaw','Population':'37,771,789','Area':'306,230'} {'Country':'Russia','Capital':'Moscow','Population':'146,047,418','Area':'16,376,870'} {'Country':'England','Capital':'London','Population':'68,529,747','Area':'241,930'} Itisevidentfromtheoutputthatcsv.DictReader()returnsadictionaryforeachrowsuchthatthecolumnheaderisthekeywhilethevalueintherowistheassociatedvalueinthedictionary. Conclusion Tosumthingsup,therearemajorlyfourdifferentwaysofaccessingspecificcolumnsfromagivencsvfile: List-BasedIndexing.Integer-BasedIndexing.Name-BasedIndexing.UsingcsvmodulesDictReaderclass. Feelfreetousetheonethatsuitsyoubest.Ihopethistutorialhelpedyou.Pleasesubscribeandstaytunedformoreinterestingtutorials.Happylearning! LearnPandastheFunWaybySolvingCodePuzzles IfyouwanttoboostyourPandasskills,considercheckingoutmypuzzle-basedlearningbookCoffeeBreakPandas(AmazonLink). Itcontains74hand-craftedPandaspuzzlesincludingexplanations.Bysolvingeachpuzzle,you’llgetascorerepresentingyourskilllevelinPandas.CanyoubecomeaPandasGrandmaster? CoffeeBreakPandasoffersafun-basedapproachtodatasciencemastery—andatrulygamifiedlearningexperience. ShubhamSayonIamaprofessionalPythonBloggerandContentcreator.Ihavepublishednumerousarticlesandcreatedcoursesoveraperiodoftime.PresentlyIamworkingasafull-timefreelancerandIhaveexperienceindomainslikePython,AWS,DevOps,andNetworking. 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