csv module in Python (2024)

The csv module provide tools for working with and manipulating csv(comma separated values)files.
csv files are used to store tabular data in plain text formats. csv data consists of records and each record is made up of fields. Normally, a line in a csv file represents a single record while fields in a record are separated by commas.

The following is an example of csv data.


Typically, files with a .csv extension are normally associated with csv data.

Reading csv data

To read data from a csv file, use the reader() function. The function has the following basic syntax:


The reader() function takes any iterable that yields a line during each iteration. This can be a file object, a list or any other iterable made of lines. It returns an iterator of lists, where each list represents a record in the csv data.

Consider if the previous csv data exists in a file called demo.csv.

import csvwith open('demo.csv') as file: reader = csv.reader(file) for row in reader: print(row)

['first_name', 'last_name', 'age', 'gender']
['John', 'Main', '30', 'male']
['Jane', 'Doe', '24', 'female']
['Morrison', 'Smith', '22', 'male']
['Brian', 'Gates', '32', 'male']
['Mary', 'Reagan', '25', 'female']

In the above example, we used data stored in a file object but as earlier mentioned, you can literally use any iterable. In the following example, we use a list instead of a file object.

import csvdata = """first_name,last_name,age,genderJohn,Main,30,maleJane,Doe,24,femaleMorrison,Smith,22,maleBrian,Gates,32,maleMary,Reagan,25,female"""reader = csv.reader(data.splitlines())for row in reader: print(row)

['first_name', 'last_name', 'age', 'gender']
['John', 'Main', '30', 'male']
['Jane', 'Doe', '24', 'female']
['Morrison', 'Smith', '22', 'male']
['Brian', 'Gates', '32', 'male']
['Mary', 'Reagan', '25', 'female']

Writing csv data

The writer() function creates an object for writing csv data. It has the following basic syntax:


Where the fileobj is the opened file where the csv data will be written to.

After creating the writer object, we can then use writerow()method to write a single record into the file or writerows() to write multiple rows at once.

import csvwith open('demo.csv', 'a') as file: writer = csv.writer(file) writer.writerow(('Ruth', 'Miller', 24, 'female')) writer.writerow(('Brandy', 'Jones', 22, 'male'))

Note that in the above example, we opened the demo.csv file with the "a" mode. This ensures that the new rows are appended to the file, if we had used the "w" mode instead, the existing rows would have been overwritten.

import csvwith open('demo.csv') as file: print(file.read())



As you can see above, the new records have been added successfully.

To add multiple rows at once, use the writer.writerows() method.

import csvrows = [ ('Stephen', 'King', 32, 'male'), ('Molly', 'Jones', 22, 'female') ]with open('demo.csv', 'a') as file: writer = csv.writer(file) writer.writerows(rows)

Quoting behavior

In cases where you want records of certain types to be stored in quotes, you can specify the quotingflag when creating the writer object. For example, if you want string items to be in quotes but numeric values not to be, you can specify the quoting parameter as csv.QUOTE_NONNUMERIC.

import csvrows = [ ('Stephen', 'King', 32, 'male'), ('Molly', 'Jones', 22, 'female') ]with open('demo.csv', 'w') as file: writer = csv.writer(file, quoting = csv.QUOTE_NONNUMERIC) writer.writerows(rows)

If you open the demo.csv file, you will see that in the written records , string fields are quoted.

import csvwith open('demo.csv') as file: print(file.read())



All the quoting options are as shown in the following table:

QUOTE_ALLQuote any field regardless of type
QUOTE_NONEDon't quote any field.
QUOTE_MINIMALQuote only fields containing some special characters. This is the default quoting behavior.
QUOTE_NONNUMERICQuote all fields except those with numeric values like integers and floats.

csv dialects

Separating csv fields with a comma is just the popular convention, there is really no well defined standard on csv format. For example, you could as well separate the fields with spaces instead of commas. This means that any csv parser needs to be very flexible when dealing with csv delimiters.

Dialects allow us to specify to the parser the delimiter and other important parameters that will be used when parsing the csv files. The csv.list_dialects() function returns a list of the registered dialects.

import csvprint(csv.list_dialects())

['excel', 'excel-tab', 'unix']

The default dialect is excel, in which the fields are delimited with commas.

If for example, you have csv data in which the fields are delimited by a pipe character "|"or any other character instead of a comma, as shown below:


You can register a new dialect using the csv.register_dialect(). You can then use the registered dialect with reader and writer objects.

import csvdata = """first_name|last_name|age|genderJohn|Main|30|maleJane|Doe|24|femaleMorrison|Smith|22|maleBrian|Gates|32|maleMary|Reagan|25|female"""csv.register_dialect('pipe', delimiter = '|')print("Dialects: ", csv.list_dialects())reader = csv.reader(data.splitlines(), dialect = 'pipe') #use the dialectfor row in reader: print(row)

Apart from delimiter, you can specify other dialect parameters depending on the nature of your csv data. The supported parameters are shown in the following tables:

parameterdefault valuedescription
delimiter,Field separator.
quotechar"The character to enclose fields wherequoting is allowed.
lineterminator\r \nThe string/character used to terminate a line
doublequoteTrueWhether quotchar parameters are doubled.
escapecharNoneCharacter used to indicate an escape sequence
skipintialspaceFalseIgnore whitespace after thedelimiter

Automatic dialect detection

In cases where the csv format is not well known, we can use the csv.Sniffer class, which automatically constructs a dialect object given a sample of csv data.

Consider if we have the following csv data.


In the above example, we used the "&" character as the delimiter.

import csvdata="""first_name&last_name&age&genderJohn&Main&30&maleJane&Doe&24&femaleMorrison&Smith&22&maleBrian&Gates&32&maleMary&Reagan&25&female"""sniffer=csv.Sniffer()dialect=sniffer.sniff(sample=data)print("delimiter: ", dialect.delimiter)reader = csv.reader(data.splitlines(), dialect = dialect)for row in reader: print(row)

As you can see, the sniffer correctly detected that "&" is used as the delimiter character. However, you should keep in mind that the sniffer is not always correct, it can only be said to be making educated guesses.

Using named fields

You can use csv.DictReader and csv.DictWriter classes to use named fields when reading or writing csv data.

The two classes allows you to specify in advance a list of field names to alias the fields in the csv data.


The DictReader class allows you to read rows with the fields having an alias name.

Consider the following example:

import csvdata = """first_name,last_name,age,genderJohn,Main,30,maleJane,Doe,24,femaleMorrison,Smith,22,maleBrian,Gates,32,maleMary,Reagan,25,female"""fields = 'fname', 'lname', 'age', 'gender'reader = csv.DictReader(data.splitlines(), fieldnames = fields)for row in reader: print(row['fname'], row['lname'])

Iterating through the DictReader object will yield a list of dictionaries, where each dictionary represents a row in the csv data.


DictWriterobjects writes data into a csv file with argument given as dictionaries. Consider the following example:

import csvfields = 'fname', 'lname', 'age', 'gender'data_to_wite = [ {'fname':'Stephen', 'lname':'King', 'age':32, 'gender':'male'}, {'age':22, 'lname':'Jones', 'fname':'Molly', 'gender':'female'},]with open('pynerds.txt', 'w') as file: writer = csv.DictWriter(file, fieldnames = fields) writer.writerows(data_to_wite)

As shown above, when using named fields, the order is not important as long as the field names are correct.

import csvwith open('demo.csv') as file: print(file.read())



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csv module in Python (2024)
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