V tej vadnici se bomo s pomočjo primerov naučili brati datoteke CSV z različnimi formati v Pythonu.
Za csvto nalogo bomo uporabili izključno modul, vgrajen v Python. Najprej pa bomo morali modul uvoziti kot:
import csv
Osnove uporabe csvmodula za branje in pisanje v datoteke CSV smo že zajeli . Če nimate pojma o uporabi csvmodula, si oglejte našo vadnico o Python CSV: branje in pisanje datotek CSV
Osnovna uporaba csv.reader ()
Oglejmo si osnovni primer uporabe csv.reader()za osvežitev obstoječega znanja.
Primer 1: Branje datotek CSV s csv.reader ()
Recimo, da imamo datoteko CSV z naslednjimi vnosi:
SN, Ime, Prispevek 1, Linus Torvalds, Linux Kernel 2, Tim Berners-Lee, World Wide Web 3, Guido van Rossum, Python Programming
Vsebino datoteke lahko beremo z naslednjim programom:
import csv with open('innovators.csv', 'r') as file: reader = csv.reader(file) for row in reader: print(row)
Izhod
('SN', 'Name', 'Contribution') ('1', 'Linus Torvalds', 'Linux Kernel') ('2', 'Tim Berners-Lee', 'World Wide Web') ('3' , 'Guido van Rossum', 'Programiranje na Python')
Tukaj smo datoteko innovators.csv odprli v načinu branja s pomočjo open()funkcije.
Če želite izvedeti več o odpiranju datotek v Pythonu, obiščite: Vnos / izhod datotek Python
Nato csv.reader()se za branje datoteke uporabi, ki vrne readerpredmet , ki ga je mogoče iti .
Nato se readerpredmet ponovi z forzanko za tiskanje vsebine vsake vrstice.
Zdaj si bomo ogledali datoteke CSV z različnimi formati. Nato se bomo naučili, kako prilagoditi csv.reader()funkcijo za njihovo branje.
Datoteke CSV z ločevalniki po meri
Privzeto se vejica uporablja kot ločilo v datoteki CSV. Vendar lahko nekatere datoteke CSV uporabljajo ločila, ki niso vejica. Nekaj priljubljenih je |in .
Recimo, da je datoteka inotors.csv v primeru 1 uporabljala jeziček kot ločilo. Za branje datoteke lahko funkciji posredujemo dodaten delimiterparameter csv.reader().
Vzemimo primer.
2. primer: Preberite datoteko CSV z ločevalnikom zavihkov
import csv with open('innovators.csv', 'r') as file: reader = csv.reader(file, delimiter = ' ') for row in reader: print(row)
Izhod
('SN', 'Name', 'Contribution') ('1', 'Linus Torvalds', 'Linux Kernel') ('2', 'Tim Berners-Lee', 'World Wide Web') ('3' , 'Guido van Rossum', 'Programiranje na Python')
Kot lahko vidimo, neobvezni parameter delimiter = ' 'pomaga določiti readerpredmet, v katerem ima datoteka CSV, iz katere beremo, zavihke kot ločilo.
Datoteke CSV z začetnimi presledki
Nekatere datoteke CSV imajo lahko po ločevalniku presledek. Ko csv.reader()za branje teh datotek CSV uporabimo privzeto funkcijo, bomo dobili tudi presledke v izhodu.
Če želimo odstraniti te začetne presledke, moramo predati dodatni parameter, imenovan skipinitialspace. Oglejmo si primer:
3. primer: Preberite datoteke CSV z začetnimi presledki
Recimo, da imamo datoteko CSV z imenom people.csv z naslednjo vsebino:
SN, ime, mesto 1, John, Washington 2, Eric, Los Angeles 3, Brad, Texas
Datoteko CSV lahko beremo na naslednji način:
import csv with open('people.csv', 'r') as csvfile: reader = csv.reader(csvfile, skipinitialspace=True) for row in reader: print(row)
Izhod
('SN', 'Name', 'City') ('1', 'John', 'Washington') ('2', 'Eric', 'Los Angeles') ('3', 'Brad', ' Teksas ')
Program je podoben drugim primerom, vendar ima dodaten skipinitialspaceparameter, ki je nastavljen na True.
To readerobjektu omogoča, da ve, da imajo vnosi začetni presledek. Posledično se odstranijo začetni presledki, ki so bili prisotni po ločevalniku.
Datoteke CSV z narekovaji
Nekatere datoteke CSV imajo lahko citate okoli vsakega ali nekaterih vnosov.
Za primer vzemimo quotes.csv z naslednjimi vnosi:
"SN", "Ime", "Citati" 1, Buda, "Kaj mislimo, da postanemo" 2, Mark Twain, "Nikoli ne obžaluj ničesar, kar te je nasmejalo" 3, Oscar Wilde, "Bodi si, vsi ostali so že zajeti
Uporaba csv.reader()v minimalnem načinu bo povzročila narekovaje.
Da jih odstranimo, bomo morali uporabiti še en neobvezni parameter, imenovan quoting.
Oglejmo si primer, kako prebrati zgornji program.
4. primer: Preberite datoteke CSV z narekovaji
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)
Izhod
('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- Specifiesreaderobject 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 thereaderobject 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 aDialectsubclass 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 returnsTrueorFalsebased 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.
Podobno je bil vzorec poslan tudi v Sniffer().sniff()funkcijo. Vrnil je vse Dialectizvedene parametre kot podrazred, ki je bil nato shranjen v spremenljivki deduced_dialect.
Kasneje smo datoteko CSV znova odprli in ji posredovali deduced_dialectspremenljivko kot parameter csv.reader().
Pravilno mogel napovedati delimiter, quotingin skipinitialspaceparametri v office.csv datoteki, ne da bi nas izrecno njihove omembe.
Opomba: Modul csv se lahko uporablja tudi za druge končnice datotek (na primer: .txt ), če je njihova vsebina v pravilni strukturi.
Priporočeno branje: pišite v datoteke CSV v Pythonu
