Čitanje CSV datoteka u Pythonu

U ovom uputstvu naučit ćemo čitati CSV datoteke s različitim formatima u Pythonu uz pomoć primjera.

csvZa ovaj ćemo zadatak koristiti isključivo modul ugrađen u Python. Ali prvo, morat ćemo uvesti modul kao:

 import csv

Već smo pokrili osnove kako koristiti csvmodul za čitanje i pisanje u CSV datoteke. Ako nemate pojma o korištenju csvmodula, pogledajte naš vodič o Python CSV: Čitanje i pisanje CSV datoteka

Osnovna upotreba csv.reader ()

Pogledajmo osnovni primjer korištenja csv.reader()za osvježavanje postojećeg znanja.

Primjer 1: Pročitajte CSV datoteke pomoću csv.reader ()

Pretpostavimo da imamo CSV datoteku sa sljedećim unosima:

 SN, ime, doprinos 1, Linus Torvalds, Linux kernel 2, Tim Berners-Lee, World Wide Web 3, Guido van Rossum, programiranje na Pythonu

Sadržaj datoteke možemo čitati pomoću sljedećeg programa:

 import csv with open('innovators.csv', 'r') as file: reader = csv.reader(file) for row in reader: print(row)

Izlaz 

 ('SN', 'Name', 'Contribution') ('1', 'Linus Torvalds', 'Linux Kernel') ('2', 'Tim Berners-Lee', 'World Wide Web') ('3' , 'Guido van Rossum', 'Programiranje na Pythonu')

Ovdje smo otvorili datoteku innovators.csv u načinu čitanja pomoću open()funkcije.

Da biste saznali više o otvaranju datoteka u Pythonu, posjetite: Ulaz / izlaz Python datoteke

Zatim csv.reader()se koristi za čitanje datoteke koja vraća iterabilni readerobjekt.

Zatim se readerobjekt ponavlja pomoću forpetlje za ispis sadržaja svakog retka.

Sada ćemo pogledati CSV datoteke s različitim formatima. Zatim ćemo naučiti kako prilagoditi csv.reader()funkciju da ih čita.

CSV datoteke s prilagođenim graničnicima

Prema zadanim postavkama zarez se koristi kao graničnik u CSV datoteci. Međutim, neke CSV datoteke mogu koristiti graničnike koji nisu zarez. Malo je popularnih |i .

Pretpostavimo da je datoteka innovators.csv u primjeru 1 koristila tab kao graničnik. Da bismo pročitali datoteku, funkciji možemo proslijediti dodatni delimiterparametar csv.reader().

Uzmimo primjer.

Primjer 2: Pročitajte CSV datoteku s razdjelnikom kartice

 import csv with open('innovators.csv', 'r') as file: reader = csv.reader(file, delimiter = ' ') for row in reader: print(row)

Izlaz 

 ('SN', 'Name', 'Contribution') ('1', 'Linus Torvalds', 'Linux Kernel') ('2', 'Tim Berners-Lee', 'World Wide Web') ('3' , 'Guido van Rossum', 'Programiranje na Pythonu')

Kao što vidimo, neobavezni parametar delimiter = ' 'pomaže u određivanju readerobjekta koji CSV datoteka iz koje čitamo ima tabulatore kao graničnik.

CSV datoteke s početnim razmacima

Neke CSV datoteke nakon graničnika mogu imati razmak. Kada koristimo zadanu csv.reader()funkciju za čitanje ovih CSV datoteka, dobit ćemo i razmake u izlazu.

Da bismo uklonili ove početne razmake, moramo proslijediti dodatni parametar tzv skipinitialspace. Pogledajmo primjer:

Primjer 3: Pročitajte CSV datoteke s početnim razmacima

Pretpostavimo da imamo CSV datoteku koja se naziva people.csv sa sljedećim sadržajem:

 SN, Ime, Grad 1, John, Washington 2, Eric, Los Angeles 3, Brad, Texas

CSV datoteku možemo čitati na sljedeći način:

 import csv with open('people.csv', 'r') as csvfile: reader = csv.reader(csvfile, skipinitialspace=True) for row in reader: print(row)

Izlaz 

 ('SN', 'Ime', 'Grad') ('1', 'John', 'Washington') ('2', 'Eric', 'Los Angeles') ('3', 'Brad', ' Teksas ')

Program je sličan ostalim primjerima, ali ima dodatni skipinitialspaceparametar koji je postavljen na True.

To omogućuje readerobjektu da zna da unosi imaju početni razmak. Kao rezultat, uklanjaju se početni razmaci koji su bili prisutni nakon graničnika.

CSV datoteke s navodnicima

Neke CSV datoteke mogu imati citate oko svakog ili nekih unosa.

Uzmimo za primjer quotes.csv sa sljedećim unosima:

 "SN", "Ime", "Citati" 1, Buddha, "Ono što mislimo da smo postali" 2, Mark Twain, "Nikad ne žalite ni zbog čega što vam se nasmiješilo" 3, Oscar Wilde, "Budi svoj, svi ostali su već zauzeti"

Korištenje csv.reader()u minimalnom načinu rezultirat će ispisom pod navodnicima.

Da bismo ih uklonili, morat ćemo upotrijebiti drugi opcijski parametar koji se naziva quoting.

Pogledajmo primjer kako čitati gornji program.

Primjer 4: Pročitajte CSV datoteke s navodnicima

 import csv with open('person1.csv', 'r') as file: reader = csv.reader(file, quoting=csv.QUOTE_ALL, skipinitialspace=True) for row in reader: print(row)

Izlaz 

 ('SN', 'Name', 'Quotes') ('1', 'Buddha', 'What we think we become') ('2', 'Mark Twain', 'Never regret anything that made you smile') ('3', 'Oscar Wilde', 'Be yourself everyone else is already taken')

As you can see, we have passed csv.QUOTE_ALL to the quoting parameter. It is a constant defined by the csv module.

csv.QUOTE_ALL specifies the reader object that all the values in the CSV file are present inside quotation marks.

There are 3 other predefined constants you can pass to the quoting parameter:

  • csv.QUOTE_MINIMAL - Specifies reader object that CSV file has quotes around those entries which contain special characters such as delimiter, quotechar or any of the characters in lineterminator.
  • csv.QUOTE_NONNUMERIC - Specifies the reader object that the CSV file has quotes around the non-numeric entries.
  • csv.QUOTE_NONE - Specifies the reader object that none of the entries have quotes around them.

Dialects in CSV module

Notice in Example 4 that we have passed multiple parameters (quoting and skipinitialspace) to the csv.reader() function.

This practice is acceptable when dealing with one or two files. But it will make the code more redundant and ugly once we start working with multiple CSV files with similar formats.

As a solution to this, the csv module offers dialect as an optional parameter.

Dialect helps in grouping together many specific formatting patterns like delimiter, skipinitialspace, quoting, escapechar into a single dialect name.

It can then be passed as a parameter to multiple writer or reader instances.

Example 5: Read CSV files using dialect

Suppose we have a CSV file (office.csv) with the following content:

 "ID"| "Name"| "Email" "A878"| "Alfonso K. Hamby"| "[email protected]" "F854"| "Susanne Briard"| "[email protected]" "E833"| "Katja Mauer"| "[email protected]"

The CSV file has initial spaces, quotes around each entry, and uses a | delimiter.

Instead of passing three individual formatting patterns, let's look at how to use dialects to read this file.

 import csv csv.register_dialect('myDialect', delimiter='|', skipinitialspace=True, quoting=csv.QUOTE_ALL) with open('office.csv', 'r') as csvfile: reader = csv.reader(csvfile, dialect='myDialect') for row in reader: print(row)

Output 

 ('ID', 'Name', 'Email') ("A878", 'Alfonso K. Hamby', '[email protected]') ("F854", 'Susanne Briard', '[email protected]') ("E833", 'Katja Mauer', '[email protected]')

From this example, we can see that the csv.register_dialect() function is used to define a custom dialect. It has the following syntax:

 csv.register_dialect(name(, dialect(, **fmtparams)))

The custom dialect requires a name in the form of a string. Other specifications can be done either by passing a sub-class of Dialect class, or by individual formatting patterns as shown in the example.

While creating the reader object, we pass dialect='myDialect' to specify that the reader instance must use that particular dialect.

The advantage of using dialect is that it makes the program more modular. Notice that we can reuse 'myDialect' to open other files without having to re-specify the CSV format.

Read CSV files with csv.DictReader()

The objects of a csv.DictReader() class can be used to read a CSV file as a dictionary.

Example 6: Python csv.DictReader()

Suppose we have a CSV file (people.csv) with the following entries:

Name Age Profession
Jack 23 Doctor
Miller 22 Engineer

Let's see how csv.DictReader() can be used.

 import csv with open("people.csv", 'r') as file: csv_file = csv.DictReader(file) for row in csv_file: print(dict(row))

Output 

 ('Name': 'Jack', ' Age': ' 23', ' Profession': ' Doctor') ('Name': 'Miller', ' Age': ' 22', ' Profession': ' Engineer')

As we can see, the entries of the first row are the dictionary keys. And, the entries in the other rows are the dictionary values.

Here, csv_file is a csv.DictReader() object. The object can be iterated over using a for loop. The csv.DictReader() returned an OrderedDict type for each row. That's why we used dict() to convert each row to a dictionary.

Notice that we have explicitly used the dict() method to create dictionaries inside the for loop.

 print(dict(row))

Note: Starting from Python 3.8, csv.DictReader() returns a dictionary for each row, and we do not need to use dict() explicitly.

The full syntax of the csv.DictReader() class is:

 csv.DictReader(file, fieldnames=None, restkey=None, restval=None, dialect='excel', *args, **kwds)

To learn more about it in detail, visit: Python csv.DictReader() class

Using csv.Sniffer class

The Sniffer class is used to deduce the format of a CSV file.

The Sniffer class offers two methods:

  • sniff(sample, delimiters=None) - This function analyses a given sample of the CSV text and returns a Dialect subclass that contains all the parameters deduced.

An optional delimiters parameter can be passed as a string containing possible valid delimiter characters.

  • has_header(sample) - This function returns True or False based on analyzing whether the sample CSV has the first row as column headers.

Let's look at an example of using these functions:

Example 7: Using csv.Sniffer() to deduce the dialect of CSV files

Suppose we have a CSV file (office.csv) with the following content:

 "ID"| "Name"| "Email" A878| "Alfonso K. Hamby"| "[email protected]" F854| "Susanne Briard"| "[email protected]" E833| "Katja Mauer"| "[email protected]"

Let's look at how we can deduce the format of this file using csv.Sniffer() class:

 import csv with open('office.csv', 'r') as csvfile: sample = csvfile.read(64) has_header = csv.Sniffer().has_header(sample) print(has_header) deduced_dialect = csv.Sniffer().sniff(sample) with open('office.csv', 'r') as csvfile: reader = csv.reader(csvfile, deduced_dialect) for row in reader: print(row)

Output 

 True ('ID', 'Name', 'Email') ('A878', 'Alfonso K. Hamby', '[email protected]') ('F854', 'Susanne Briard', '[email protected]') ('E833', 'Katja Mauer', '[email protected]')

As you can see, we read only 64 characters of office.csv and stored it in the sample variable.

This sample was then passed as a parameter to the Sniffer().has_header() function. It deduced that the first row must have column headers. Thus, it returned True which was then printed out.

Slično tome, uzorak je također proslijeđen Sniffer().sniff()funkciji. Vratio je sve izvedene parametre kao Dialectpotklasu koja je zatim spremljena u varijablu deduced_dialect.

Kasnije smo ponovno otvorili CSV datoteku i proslijedili deduced_dialectvarijablu kao parametar csv.reader().

Bilo je točno mogli predvidjeti delimiter, quotinga skipinitialspaceparametri u office.csv datoteke bez nas ih izričito spominje.

Napomena: csv modul može se koristiti i za druge nastavke datoteka (poput: .txt ) sve dok je njihov sadržaj u ispravnoj strukturi.

Preporučeno čitanje: Zapišite u CSV datoteke na Pythonu

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