What is the best way to read a csv file in python?

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

Table of Contents:

1. What is a CSV?
2. Reading a CSV
3. Writing to a CSV

1. What is a CSV?

CSV stands for “Comma Separated Values.” It is the simplest form of storing data in tabular form as plain text. It is important to know to work with CSV because we mostly rely on CSV data in our day-to-day lives as data scientists.

Structure of CSV: 

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

After the header, each line of the file is an observation/a record. The values of a record are separated by “comma.”

2. Reading a CSV

CSV files can be handled in multiple ways in Python.

2.1 Using csv.reader

Reading a CSV using Python’s inbuilt module called csv using csv.reader object.

Steps to read a CSV file:

1. Import the csv library

import csv

2. Open the CSV file

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

file = open('Salary_Data.csv')

type(file)

The type of file is “_io.TextIOWrapper” which is a file object that is returned by the open() method.

3. Use the csv.reader object to read the CSV file

csvreader = csv.reader(file)

4. Extract the field names

Create an empty list called header. Use the next() method to obtain the header.

The .next() method returns the current row and moves to the next 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 = next(csvreader)
header

5. Extract the rows/records

Create an empty list 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

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

file.close()

Complete Code:

Python Code:

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

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) as file:
    csvreader = csv.reader(file)
    header = next(csvreader)
    for row in csvreader:
        rows.append(row)
print(header)
print(rows)

2.2 Using .readlines()

Now the question is – “Is it possible to fetch the header, rows using only open() and with() statements and without the csv library?” Let’s see…

.readlines() method is the answer. It returns all the lines in a file as a list. Each item of the list 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[:1]
rows = content[1:]
print(header)
print(rows)

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

What if we have 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.

2.3 Using pandas

Steps of reading CSV files using pandas

1. Import pandas library

import pandas as pd

2. 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. Extract the rows

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

data.Salary

3. Writing to a CSV file

We can write to a CSV file in multiple ways.

3.1 Using csv.writer

Let’s assume we are recording 3 Students data(Name, M1 Score, M2 Score)

header = ['Name', 'M1 Score', 'M2 Score']
data = [['Alex', 62, 80], ['Brad', 45, 56], ['Joey', 85, 98]]

Steps of writing to a CSV file:

1. Import csv library

import csv

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

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

4. Write the header

5. Write the rest of the data

code for steps 2-5

filename = 'Students_Data.csv'
with open(filename, 'w', newline="") as 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 data

Below is how our CSV file looks.

3.2 Using .writelines()

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

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

3. Write to a CSV file using to_csv()

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

**separator is ‘,’ by default.

index=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. By the end of this article, we are familiar with different ways of handling CSV files in Python.

I hope this article is informative. Feel free to share it with your study buddies.

References:

Check out the complete code from the GitHub repo.

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