Reading & Writing CSV Files0%
Working with JSON Files

Reading & Writing CSV Files

Beginner12 min read•Updated: Jul 11, 2026
Study Materials

Reading & Writing CSV Files in Python

CSV (Comma-Separated Values) is one of the most ubiquitous plain-text data interchange formats across software engineering, data science, and business analytics. Python provides robust, highly optimized native support for reading, writing, and parsing CSV documents through its built-in csv standard library module.

1. What is a CSV File?

A CSV file stores tabular data (numbers and text) in plain text form, where each line represents a data record (row) and each record consists of one or more fields separated by a delimiter character (most commonly a comma ,).

Output
id,name,role,department
101,Aarav Sharma,Software Engineer,Engineering
102,Priya Patel,Data Scientist,Analytics
103,Rohan Verma,Product Manager,Product

While you could theoretically read a CSV using basic split(',') strings, commas frequently occur inside quoted fields (e.g., "New Delhi, India" or "Engineering, Platform"). Python's csv module automatically handles escaping, quotes, delimiters, and line endings.

2. Reading CSV Files with csv.reader

The csv.reader object takes an iterable file stream and yields each row as a list of strings.

Basic Reader Example

Python
import csv
 
# Open file with utf-8 encoding and newline=''
with open("employees.csv", mode="r", encoding="utf-8", newline="") as file:
reader = csv.reader(file)
# Read the header row separately
header = next(reader)
print(f"Header: {header}")
# Iterate through remaining data rows
for row in reader:
emp_id, name, role, dept = row
print(f"[{emp_id}] {name} -> {role} ({dept})")

Key Considerations:

  1. 1
    next(reader): Moves the iterator past the header line so the loop only processes data records.
  2. 2
    String Types: All extracted values are strings. You must explicitly convert numbers (int(row[0]) or float(row[3])).

3. Writing CSV Files with csv.writer

The csv.writer object provides methods to write sequences of data as formatted CSV rows: writerow() for a single row and writerows() for a collection of rows.

Python
import csv
 
headers = ["Name", "Score", "Grade"]
students = [
["Aarav", 92, "A"],
["Diya", 85, "B"],
["Kavya", 97, "A+"]
]
 
# CRITICAL: Always use newline='' when opening files for writing with csv
with open("grades.csv", mode="w", encoding="utf-8", newline="") as file:
writer = csv.writer(file)
# Write the column headings
writer.writerow(headers)
# Write all data rows in a single call
writer.writerows(students)
 
print("Grades written successfully.")
Why newline='' is mandatory: On Windows systems, default file writing translates \n to \r\n. Without newline='', the csv module adds its own \r\n, resulting in blank empty lines between every row. Specifying newline='' allows the csv module to control line terminators correctly across all platforms.

4. Dictionary-Based CSV Handling: DictReader & DictWriter

Working with lists relies on positional indexing (row[1]), which easily breaks if columns change order. Python's csv.DictReader and csv.DictWriter map CSV rows directly to Python dictionaries.

Reading with csv.DictReader

csv.DictReader treats the first row as field names (keys) and returns each subsequent row as an OrderedDict or standard dict.

Python
import csv
 
with open("employees.csv", mode="r", encoding="utf-8", newline="") as file:
reader = csv.DictReader(file)
print(f"Detected columns: {reader.fieldnames}")
for row in reader:
# Access columns cleanly by key name
print(f"Employee: {row['name']} | Dept: {row['department']}")

Writing with csv.DictWriter

When writing dictionaries, specify fieldnames and call writeheader() before appending rows.

Python
import csv
 
fieldnames = ["id", "title", "price", "in_stock"]
 
catalog = [
{"id": "P01", "title": "Wireless Mouse", "price": 24.99, "in_stock": True},
{"id": "P02", "title": "Mechanical Keyboard", "price": 79.50, "in_stock": True},
{"id": "P03", "title": "USB-C Hub", "price": 35.00, "in_stock": False}
]
 
with open("products.csv", mode="w", encoding="utf-8", newline="") as file:
writer = csv.DictWriter(file, fieldnames=fieldnames)
# Writes the header row matching fieldnames
writer.writeheader()
# Write individual rows or batch
writer.writerows(catalog)
 
print("Product catalog saved.")

5. Custom Delimiters and Quoting Options

CSV files often use alternative delimiters like tabs (TSV), semicolons (common in European Excel), or pipes (|).

Python
import csv
 
# Reading a semicolon-separated file
with open("data_eu.csv", mode="r", encoding="utf-8", newline="") as f:
reader = csv.reader(f, delimiter=";")
for row in reader:
print(row)
 
# Custom quoting: Quote all non-numeric fields
with open("output.csv", mode="w", encoding="utf-8", newline="") as f:
writer = csv.writer(f, delimiter="|", quoting=csv.QUOTE_NONNUMERIC)
writer.writerow(["Item", 450, "Electronics"])

Quoting Constants in csv:

  • csv.QUOTE_MINIMAL (default): Quotes fields only if they contain the delimiter, quote char, or line break.
  • csv.QUOTE_ALL: Quotes every single field regardless of content.
  • csv.QUOTE_NONNUMERIC: Quotes all string fields and automatically casts unquoted items to floats when reading.
  • csv.QUOTE_NONE: Never quotes fields; requires an escape character if the delimiter appears inside text.

6. Summary Comparison Table

Featurecsv.reader / csv.writercsv.DictReader / csv.DictWriter
Data FormatList of strings (['101', 'Aarav'])Dictionary ({'id': '101', 'name': 'Aarav'})
IndexingNumeric index (row[0])Column key (row['id'])
Refactor SafetyLow (column shifts break index)High (columns can appear in any order)
PerformanceSlightly faster, lower memoryMinor overhead for dict creation
Best ForPure mathematical/raw data arraysStructured records and entity objects

Multiple Choice Questions

1. What is the primary purpose of specifying newline='' when opening a file for the csv module?

A. To prevent Python from raising an IOError during read mode B. To prevent duplicate carriage return characters (\r\r\n) and blank lines on Windows C. To force the CSV module to only parse single-line files D. To automatically remove trailing spaces from every string value Answer: B Explanation: On Windows systems, Python's default text mode converts \n to \r\n. Without newline='', Python and the csv writer both append carriage returns, resulting in unwanted empty lines between rows.

2. Which method from csv.reader skips or extracts the first row containing column titles?

A. reader.skip_header() B. reader.pop(0) C. next(reader) D. reader.read_header() Answer: C Explanation: csv.reader is a Python iterator. Calling next(reader) consumes and returns the first row (the header), advancing the iterator to the second row.

3. When using csv.DictWriter, what method must be called to write the column names into the first line?

A. writer.write_columns() B. writer.writeheader() C. writer.writerow_headers() D. writer.create_schema() Answer: B Explanation: writer.writeheader() writes a row with the field names specified during csv.DictWriter(file, fieldnames=...) initialization.

4. Which quoting mode quotes every string field and treats unquoted numbers as floats?

A. csv.QUOTE_ALL B. csv.QUOTE_MINIMAL C. csv.QUOTE_NONE D. csv.QUOTE_NONNUMERIC Answer: D Explanation: csv.QUOTE_NONNUMERIC instructs the writer to quote all non-numeric fields and instructs the reader to convert all unquoted fields to floats.

5. What data structure does each iteration over a csv.DictReader yield?

A. A tuple of column values B. A dictionary mapping header names to row values C. A list of characters D. A string formatted in JSON Answer: B Explanation: csv.DictReader parses the header line and maps each subsequent row's values to their respective header keys as a dictionary.

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