Working with csv files in Python

This article explains how to load and parse a CSV file in Python.

First of all, what is a CSV ?
CSV (Comma Separated Values) is a simple file format used to store tabular data, such as a spreadsheet or database. A CSV file stores tabular data (numbers and text) in plain text. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. The use of the comma as a field separator is the source of the name for this file format.

For working CSV files in python, there is an inbuilt module called csv.

Reading a CSV file

# importing csv module 
import csv 

# csv file name 
filename = "aapl.csv"

# initializing the titles and rows list 
fields = [] 
rows = [] 

# reading csv file 
with open(filename, 'r') as csvfile: 
	# creating a csv reader object 
	csvreader = csv.reader(csvfile) 
	
	# extracting field names through first row 
	fields = next(csvreader) 

	# extracting each data row one by one 
	for row in csvreader: 
		rows.append(row) 

	# get total number of rows 
	print("Total no. of rows: %d"%(csvreader.line_num)) 

# printing the field names 
print('Field names are:' + ', '.join(field for field in fields)) 

# printing first 5 rows 
print('\nFirst 5 rows are:\n') 
for row in rows[:5]: 
	# parsing each column of a row 
	for col in row: 
		print("%10s"%col), 
	print('\n') 

The output of above program looks like this:

The above example uses a CSV file aapl.csv which can be downloaded from here.
Run this program with the aapl.csv file in same directory.

Let us try to understand this piece of code.

• with open(filename, ‘r’) as csvfile: csvreader = csv.reader(csvfile)Here, we first open the CSV file in READ mode. The file object is named as csvfile. The file object is converted to csv.reader object. We save the csv.reader object as csvreader.
• fields = csvreader.next()csvreader is an iterable object. Hence, .next() method returns the current row and advances the iterator to the next row. Since the first row of our csv file contains the headers (or field names), we save them in a list called fields.
• for row in csvreader: rows.append(row)Now, we iterate through remaining rows using a for loop. Each row is appended to a list called rows. If you try to print each row, one can find that row is nothing but a list containing all the field values.
• print(“Total no. of rows: %d”%(csvreader.line_num))csvreader.line_num is nothing but a counter which returns the number of rows which have been iterated.

Writing to a CSV file

# importing the csv module 
import csv 

# field names 
fields = ['Name', 'Branch', 'Year', 'CGPA'] 

# data rows of csv file 
rows = [ ['Nikhil', 'COE', '2', '9.0'], 
		['Sanchit', 'COE', '2', '9.1'], 
		['Aditya', 'IT', '2', '9.3'], 
		['Sagar', 'SE', '1', '9.5'], 
		['Prateek', 'MCE', '3', '7.8'], 
		['Sahil', 'EP', '2', '9.1']] 

# name of csv file 
filename = "university_records.csv"

# writing to csv file 
with open(filename, 'w') as csvfile: 
	# creating a csv writer object 
	csvwriter = csv.writer(csvfile) 
	
	# writing the fields 
	csvwriter.writerow(fields) 
	
	# writing the data rows 
	csvwriter.writerows(rows)

Let us try to understand the above code in pieces.

• fields and rows have been already defined. fields is a list containing all the field names. rows is a list of lists. Each row is a list containing the field values of that row.
• with open(filename, ‘w’) as csvfile: csvwriter = csv.writer(csvfile)Here, we first open the CSV file in WRITE mode. The file object is named as csvfile. The file object is converted to csv.writer object. We save the csv.writer object as csvwriter.
• csvwriter.writerow(fields)Now we use writerow method to write the first row which is nothing but the field names.
• csvwriter.writerows(rows)We use writerows method to write multiple rows at once.

Writing a dictionary to a CSV file

# importing the csv module 
import csv 

# my data rows as dictionary objects 
mydict =[{'branch': 'COE', 'cgpa': '9.0', 'name': 'Nikhil', 'year': '2'}, 
		{'branch': 'COE', 'cgpa': '9.1', 'name': 'Sanchit', 'year': '2'}, 
		{'branch': 'IT', 'cgpa': '9.3', 'name': 'Aditya', 'year': '2'}, 
		{'branch': 'SE', 'cgpa': '9.5', 'name': 'Sagar', 'year': '1'}, 
		{'branch': 'MCE', 'cgpa': '7.8', 'name': 'Prateek', 'year': '3'}, 
		{'branch': 'EP', 'cgpa': '9.1', 'name': 'Sahil', 'year': '2'}] 

# field names 
fields = ['name', 'branch', 'year', 'cgpa'] 

# name of csv file 
filename = "university_records.csv"

# writing to csv file 
with open(filename, 'w') as csvfile: 
	# creating a csv dict writer object 
	writer = csv.DictWriter(csvfile, fieldnames = fields) 
	
	# writing headers (field names) 
	writer.writeheader() 
	
	# writing data rows 
	writer.writerows(mydict) 

In this example, we write a dictionary mydict to a CSV file.

• with open(filename, ‘w’) as csvfile: writer = csv.DictWriter(csvfile, fieldnames = fields)Here, the file object (csvfile) is converted to a DictWriter object.
  Here, we specify the fieldnames as an argument.
• writer.writeheader()writeheader method simply writes the first row of your csv file using the pre-specified fieldnames.
• writer.writerows(mydict)writerows method simply writes all the rows but in each row, it writes only the values(not keys).

So, in the end, our CSV file looks like this:

Important Points:

• In csv modules, an optional dialect parameter can be given which is used to define a set of parameters specific to a particular CSV format. By default, csv module uses excel dialect which makes them compatible with excel spreadsheets. You can define your own dialect using register_dialect method.
  Here is an example:

     csv.register_dialect(
    'mydialect',
    delimiter = ',',
    quotechar = '"',
    doublequote = True,
    skipinitialspace = True,
    lineterminator = '\r\n',
    quoting = csv.QUOTE_MINIMAL)

Now, while defining a csv.reader or csv.writer object, we can specify the dialect like
this:

csvreader = csv.reader(csvfile, dialect='mydialect')

• Now, consider that a CSV file looks like this in plain-text:
  We notice that the delimiter is not a comma but a semi-colon. Also, the rows are separated by two newlines instead of one. In such cases, we can specify the delimiter and line terminator as follows:csvreader = csv.reader(csvfile, delimiter = ';', lineterminator = '\n\n')