8.2 Reading CSV Files in Python

CSV (Comma-Separated Values) files are a ubiquitous format for storing tabular data. Python's built-in csv module offers a straightforward and efficient way to read data from these files, whether you're importing from spreadsheets or processing exported datasets.

This guide focuses on using the csv.reader() method for various CSV reading tasks.

1. Importing the csv Module

Before you can read CSV files, you need to import the csv module. This module is included with Python's standard library, so no additional installation is required.

import csv

2. Understanding csv.reader()

The csv.reader() function reads a CSV file line by line, treating each line as a row and splitting it into a list of strings based on a specified delimiter.

Syntax:

csv.reader(file_object, delimiter=',')

Parameters:

• file_object: The file object opened in read mode ('r').
• delimiter: The character used to separate fields within each row. The default is a comma (,).

3. Reading a Simple CSV File

This example demonstrates how to read a standard CSV file where fields are separated by commas.

Example (employees.csv):

Name,Department,Joining Year
Rahul,IT,2022
Anjali,HR,2021
Suman,Finance,2023

Python Code:

import csv

with open('employees.csv', 'r', newline='') as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

Explanation:

1. The employees.csv file is opened in read mode ('r'). The newline='' argument is crucial to prevent blank rows when reading on some operating systems.
2. A csv.reader object is created to iterate over the file's contents.
3. Each row is a list of strings, representing the fields in that row, and is then printed.

Sample Output:

['Name', 'Department', 'Joining Year']
['Rahul', 'IT', '2022']
['Anjali', 'HR', '2021']
['Suman', 'Finance', '2023']

4. Skipping the Header Row

If your CSV file includes a header row that you wish to exclude from your data processing, you can use the next() function.

Python Code:

import csv

with open('employees.csv', 'r', newline='') as file:
    reader = csv.reader(file)
    next(reader)  # Skip the header row
    for row in reader:
        print(row)

Explanation:

The next(reader) function advances the iterator by one step, effectively consuming and discarding the first row (the header) before the loop begins processing the actual data rows.

5. Reading with a Custom Delimiter

CSV files may use characters other than commas as delimiters, such as semicolons (;) or pipes (|). You can specify a different delimiter when creating the csv.reader object.

Example (results.csv):

Student;Math;Science;English
Alice;85;92;78
Bob;76;88;90

Python Code:

import csv

with open('results.csv', 'r', newline='') as file:
    reader = csv.reader(file, delimiter=';')
    for row in reader:
        print(row)

Use Case: This is particularly useful when working with European-formatted CSV files or data exported from systems that use alternative separators.

6. Storing CSV Data into a List

You can load the entire content of a CSV file into a Python list, where each element of the list is another list representing a row.

Example (courses.csv):

Course,Credits,Instructor
Introduction to Python,3,Dr. Smith
Data Structures,4,Prof. Jones
Algorithms,3,Dr. Lee

Python Code:

import csv

with open('courses.csv', 'r', newline='') as file:
    reader = csv.reader(file)
    data = list(reader)

print(data)

Output:

The data variable will hold a list of lists:

[['Course', 'Credits', 'Instructor'], ['Introduction to Python', '3', 'Dr. Smith'], ['Data Structures', '4', 'Prof. Jones'], ['Algorithms', '3', 'Dr. Lee']]

Summary of Common CSV Reading Tasks

Conclusion

Python's csv module, particularly the csv.reader() method, simplifies the process of reading CSV files. It provides flexibility to handle different delimiters and to easily extract data into usable Python data structures.

For further exploration into Python's file handling, data analysis capabilities, and automation techniques, consult additional resources.

Interview Questions on Reading CSV Files in Python

• How do you read a CSV file in Python using the csv module? You import the csv module and use csv.reader() on an opened file object.
• What does the csv.reader() function return when reading a CSV file? It returns an iterator where each item yielded is a list of strings, representing a row from the CSV file.
• How can you skip the header row while reading a CSV file in Python? After creating the csv.reader object, you can call next() on it once before iterating through the rows.
• How do you specify a custom delimiter when reading a CSV file? You pass the delimiter argument to the csv.reader() function, e.g., csv.reader(file, delimiter=';').
• Explain how to read a CSV file and store its contents in a list. Open the file, create a csv.reader object, and then use list() to convert the reader iterator into a list of lists.
• What are some common use cases for reading CSV files in Python? Data import for analysis, configuration file processing, exporting data from databases or web services, and data migration.
• How do you handle CSV files with different encodings or newline characters? Specify the encoding parameter when opening the file (e.g., open('file.csv', 'r', encoding='utf-8')) and use newline='' to prevent issues with line endings.
• Can you compare the csv.reader() method with other libraries like pandas for reading CSV files? csv.reader is part of the standard library, lightweight, and good for basic row-by-row processing. pandas offers more powerful data manipulation capabilities, higher performance for large datasets, and sophisticated data structures like DataFrames.
• How do you handle errors or malformed rows when reading CSV files? You can use try-except blocks within your loop to catch potential errors (e.g., ValueError if a row doesn't have the expected number of fields) and decide how to handle them (skip, log, etc.).
• How would you read a large CSV file efficiently without loading it entirely into memory? Process the file row by row using the csv.reader iterator directly within a loop, rather than converting the entire file to a list first.