CSV-failide lugemine Pythonis

Selles õpetuses õpime näidete abil lugema Pythonis erineva vorminguga CSV-faile.

csvSelle ülesande jaoks kasutame eranditult Pythoni sisseehitatud moodulit. Kuid kõigepealt peame mooduli importima järgmiselt:

 import csv

Oleme juba käsitlenud põhitõdesid, kuidas csvmoodulit kasutada CSV-failide lugemiseks ja kirjutamiseks. Kui teil pole csvmooduli kasutamise kohta ideed , vaadake meie õpetust Python CSV-st: CSV-failide lugemine ja kirjutamine

Csv.reader () põhikasutus

Vaatame csv.reader()põhinäidet olemasolevate teadmiste värskendamiseks.

Näide 1: CSV-failide lugemine csv.reader () abil

Oletame, et meil on CSV-fail järgmiste kirjetega:

 SN, nimi, kaastöö 1, Linus Torvalds, Linuxi tuum 2, Tim Berners-Lee, veeb 3, Guido van Rossum, Pythoni programmeerimine

Faili sisu saame lugeda järgmise programmiga:

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

Väljund 

 ('SN', 'Nimi', 'Kaastöö') ('1', 'Linus Torvalds', 'Linuxi tuum') ('2', 'Tim Berners-Lee', 'Interneti-ühendus') ('3' ("Guido van Rossum", "Pythoni programmeerimine")

Siin oleme funktsiooni abil avanud faili innovators.csv lugemisrežiimis open().

Failide Pythonis avamise kohta lisateabe saamiseks külastage lehte Python File Input / Output

Seejärel csv.reader()kasutatakse faili lugemiseks, mis tagastab korduva readerobjekti.

readerObjekti Seejärel kordasid kasutades forloop printida sisu iga rida.

Nüüd vaatame erineva vorminguga CSV-faile. Seejärel õpime csv.reader()funktsiooni nende lugemiseks kohandama .

CSV-failid kohandatud eraldajatega

Vaikimisi kasutatakse CSV-failis eraldajat koma. Mõnes CSV-failis saab aga kasutada komakohti lisaks eraldusmärkidele. Vähesed populaarsed on |ja .

Oletame, et näites 1 toodud fail innovators.csv kasutas eraldajana vahekaarti . Faili lugemiseks võime funktsioonile edastada täiendava parameetri .delimitercsv.reader()

Võtame näite.

Näide 2: CSV-faili lugemine, kasutades vahekaardi eraldajat

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

Väljund 

 ('SN', 'Nimi', 'Kaastöö') ('1', 'Linus Torvalds', 'Linuxi tuum') ('2', 'Tim Berners-Lee', 'Interneti-ühendus') ('3' ("Guido van Rossum", "Pythoni programmeerimine")

Nagu näeme, delimiter = ' 'aitab valikuline parameeter määrata readerobjekti, millest CSV-fail, mida loeme, on eraldajatena vahekaardid .

Esialgsete tühikutega CSV-failid

Mõnel CSV-failil võib pärast eraldajat olla tühik. Kui kasutame csv.reader()nende CSV-failide lugemiseks vaikefunktsiooni , saame väljundis ka tühikud.

Nende esialgsete tühikute eemaldamiseks peame edastama täiendava parameetri nimega skipinitialspace. Vaatame näidet:

Näide 3: algsete tühikutega CSV-failide lugemine

Oletame, et meil on järgmise sisuga CSV-fail nimega people.csv :

 SN, nimi, linn 1, John, Washington 2, Eric, Los Angeles 3, Brad, Texas

CSV-faili saame lugeda järgmiselt:

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

Väljund 

 ("SN", "Nimi", "Linn") ("1", "John", "Washington") ("2", "Eric", "Los Angeles") ("3", "Brad", " Texas ')

Programm sarnaneb teiste näidetega, kuid sellel on täiendav skipinitialspaceparameeter, mille väärtuseks on seatud True.

See võimaldab readerobjektil teada, et sisestustel on esialgne tühik. Selle tulemusel eemaldatakse algsed tühikud, mis olid pärast eraldajat.

Jutumärkidega CSV-failid

Mõnes CSV-failis võivad olla iga jutu või mõne kirje ümber jutumärgid.

Võtame näiteks quotes.csv järgmiste kirjetega:

 "SN", "Nimi", "Tsitaadid" 1, Buddha, "Milleks me arvame, et me saame" 2, Mark Twain, "Ärge kunagi kahetse midagi, mis pani teid naeratama" 3, Oscar Wilde, "Ole ise, kõik teised on juba võetud"

Kasutades csv.reader()minimaalselt annab tulemuseks väljundi jutumärkideta.

Nende eemaldamiseks peame kasutama veel ühte valikulist parameetrit nimega quoting.

Vaatame ülaltoodud programmi lugemise näidet.

Näide 4: lugege jutumärkidega CSV-faile

 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)

Väljund 

 ('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.

Samamoodi edastati proov ka Sniffer().sniff()funktsioonile. See tagastas kõik tuletatud parameetrid Dialectalaklassina, mis seejärel salvestati muutujale deduced_dialect.

Hiljem avasime uuesti CSV-faili ja edastasime deduced_dialectmuutuja parameetrina csv.reader().

See suutis korrektselt faili office.csv ja parameetreid ennustada delimiter, ilma et me neid selgesõnaliselt mainiksime .quotingskipinitialspace

Märkus . Csv-moodulit saab kasutada ka teiste faililaiendite (näiteks: .txt ) jaoks, kui nende sisu on õiges struktuuris.

Soovitatav lugemine: kirjutage Pythonis CSV-failidesse

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