Reading Csv File Line by Line in Python

Intro: In this article, I will walk y'all through the different ways of reading and writing CSV files in Python.

i. What is a CSV?

CSV stands for "Comma Separated Values." It is the simplest form of storing information in tabular course every bit patently text. It is of import to know to work with CSV because we mostly rely on CSV data in our twenty-four hours-to-day lives as information scientists.

Structure of CSV:

We have a file named "Salary_Data.csv." The commencement line of a CSV file is the header and contains the names of the fields/features.

Later the header, each line of the file is an observation/a record. The values of a tape are separated by "comma."

2. Reading a CSV

CSV files tin can be handled in multiple ways in Python.

two.one Using csv.reader

Reading a CSV using Python'southward inbuilt module called csv using csv.reader object.

Steps to read a CSV file:

1. Import the csv library

import csv

two. Open the CSV file

The .open() method in python is used to open up files and return a file object.

file = open up('Salary_Data.csv')  blazon(file)

The blazon of file is "_io.TextIOWrapper" which is a file object that is returned by the open() method.

iii. Utilize the csv.reader object to read the CSV file

csvreader = csv.reader(file)

4. Extract the field names

Create an empty list called header. Employ the side by side() method to obtain the header.

The .adjacent() method returns the current row and moves to the side by side row.

The first time you run next() it returns the header and the next time you run it returns the first record and so on.

header = [] header = adjacent(csvreader) header

5. Excerpt the rows/records

Create an empty listing called rows and iterate through the csvreader object and append each row to the rows list.

rows = [] for row in csvreader:         rows.append(row) rows

6. Close the file

.shut() method is used to close the opened file. Once it is closed, we cannot perform any operations on it.

file.close()

Complete Code:

import csv file = open("Salary_Data.csv") csvreader = csv.reader(file) header = next(csvreader) impress(header) rows = [] for row in csvreader:     rows.suspend(row) print(rows) file.shut()

Naturally, we might forget to close an open file. To avoid that nosotros can apply the with()argument to automatically release the resources. In simple terms, there is no need to call the .shut() method if nosotros are using with() argument.

Implementing the above code using with() statement:

Syntax: with open(filename, mode) as alias_filename:

Modes:

'r' – to read an existing file,
'w' – to create a new file if the given file doesn't exist and write to it,
'a' – to append to existing file content,
'+' – to create a new file for reading and writing

import csv rows = [] with open("Salary_Data.csv", 'r) every bit file:     csvreader = csv.reader(file)     header = side by side(csvreader)     for row in csvreader:         rows.append(row) impress(header) print(rows)

2.ii Using .readlines()

Now the question is – "Is information technology possible to fetch the header, rows using merely open() and with() statements and without the csv library?" Let's see…

.readlines() method is the answer. Information technology returns all the lines in a file as a list. Each detail of the listing is a row of our CSV file.

The first row of the file.readlines() is the header and the rest of them are the records.

with open('Salary_Data.csv') as file:     content = file.readlines() header = content[:i] rows = content[1:] impress(header) impress(rows)

**The 'n' from the output can be removed using .strip() method.

What if we take a huge dataset with hundreds of features and thousands of records. Would it be possible to handle lists??

Here comes the pandas library into the picture.

two.3 Using pandas

Steps of reading CSV files using pandas

1. Import pandas library

import pandas as pd

ii. Load CSV files to pandas using read_csv()

Basic Syntax: pandas.read_csv(filename, delimiter=',')

data= pd.read_csv("Salary_Data.csv") data

3. Extract the field names

.columns is used to obtain the header/field names.

data.columns

4. Excerpt the rows

All the data of a data frame can exist accessed using the field names.

data.Bacon

3. Writing to a CSV file

We tin can write to a CSV file in multiple ways.

iii.1 Using csv.writer

Let's presume we are recording 3 Students information(Name, M1 Score, M2 Score)

header = ['Name', 'M1 Score', 'M2 Score'] data = [['Alex', 62, lxxx], ['Brad', 45, 56], ['Joey', 85, 98]]

Steps of writing to a CSV file:

one. Import csv library

import csv

2. Ascertain a filename and Open the file using open()

3. Create a csvwriter object using csv.writer()

4. Write the header

5. Write the residue of the information

code for steps 2-5

filename = 'Students_Data.csv' with open(filename, 'w', newline="") every bit file:     csvwriter = csv.writer(file) # 2. create a csvwriter object     csvwriter.writerow(header) # 4. write the header     csvwriter.writerows(data) # 5. write the rest of the information

Below is how our CSV file looks.

3.2 Using .writelines()

Iterate through each listing and catechumen the list elements to a string and write to the csv file.

header = ['Proper name', 'M1 Score', 'M2 Score'] data = [['Alex', 62, 80], ['Brad', 45, 56], ['Joey', 85, 98]] filename = 'Student_scores.csv' with open up(filename, 'w') as file:     for header in header:         file.write(str(header)+', ')     file.write('n')     for row in data:         for 10 in row:             file.write(str(x)+', ')         file.write('n')

3.3. Using pandas

Steps to writing to a CSV using pandas

1. Import pandas library

import pandas as pd

2. Create a pandas dataframe using pd.DataFrame

Syntax: pd.DataFrame(data, columns)

The data parameter takes the records/observations and the columns parameter takes the columns/field names.

header = ['Name', 'M1 Score', 'M2 Score'] data = [['Alex', 62, 80], ['Brad', 45, 56], ['Joey', 85, 98]] data = pd.DataFrame(data, columns=header)

three. Write to a CSV file using to_csv()

Syntax: DataFrame.to_csv(filename, sep=',', index=False)

**separator is ',' by default.

alphabetize=False to remove the index numbers.

data.to_csv('Stu_data.csv', index=False)

Below is how our CSV looks like

End Notes:

Thank you for reading till the conclusion. Past the finish of this article, we are familiar with dissimilar means of handling CSV files in Python.

I hope this article is informative. Experience costless to share information technology with your study buddies.

References:

Cheque out the consummate lawmaking from the GitHub repo.