Reading & Writing CSV Files

CSV (Comma-Separated Values) is the simplest, most universal data format on the planet — every spreadsheet tool, database, and analytics platform speaks it. Python's built-in csv module makes reading and writing CSVs trivial, while pandas takes it to industrial strength.

• Import the module: import csv.
• Read with csv.reader(file) or csv.DictReader(file) for dictionaries.
• Write with csv.writer(file) or csv.DictWriter(file, fieldnames=...).
• Always open CSV files with newline="" to avoid blank lines on Windows.

import csv

# write
rows = [["name", "age"], ["Riya", 22], ["Sam", 19]]
with open("people.csv", "w", newline="", encoding="utf-8") as f:
    csv.writer(f).writerows(rows)

# read as lists
with open("people.csv", encoding="utf-8") as f:
    for row in csv.reader(f):
        print(row)

# read as dicts
with open("people.csv", encoding="utf-8") as f:
    for rec in csv.DictReader(f):
        print(rec["name"], rec["age"])

• Why CSV?: CSV is plain text — human-readable, easy to debug, and supported by Excel, Google Sheets, SQLite, pandas, and virtually every analytics tool.
• csv.reader: Reads rows as lists of strings. Quoted fields, embedded commas, and escaped quotes are handled for you.
• csv.writer: Writes a list (or list of lists) to a file. Use writerow for one row and writerows for many.
• DictReader & DictWriter: Treat each row as a dictionary keyed by header names. Much more readable than tracking column indices.
• Delimiters & quoting: Use delimiter='\t' for TSV, delimiter=';' for European CSVs. quoting=csv.QUOTE_ALL quotes every field.
• pandas for big data: pd.read_csv("file.csv") reads megabytes in one line and returns a DataFrame with columns, dtypes, and indexing.

import csv
rows = [["name", "marks"], ["Riya", 92], ["Sam", 87], ["Anu", 78]]
with open("marks.csv", "w", newline="", encoding="utf-8") as f:
    csv.writer(f).writerows(rows)
print("Done")

import csv
with open("marks.csv", encoding="utf-8") as f:
    for row in csv.reader(f):
        print(row)

import csv
with open("marks.csv", encoding="utf-8") as f:
    for rec in csv.DictReader(f):
        print(rec["name"], "->", rec["marks"])

import csv
data = [{"name":"Riya","age":22}, {"name":"Sam","age":19}]
with open("dw.csv", "w", newline="", encoding="utf-8") as f:
    w = csv.DictWriter(f, fieldnames=["name", "age"])
    w.writeheader()
    w.writerows(data)
print("Saved")

import csv
total = count = 0
with open("marks.csv", encoding="utf-8") as f:
    for rec in csv.DictReader(f):
        total += int(rec["marks"])
        count += 1
print("Average:", total / count)

import pandas as pd
df = pd.read_csv("marks.csv")
print(df)
print("Mean marks:", df["marks"].mean())

• Always pass newline="" when opening CSVs to avoid extra blank lines on Windows.
• Use encoding="utf-8" consistently — non-ASCII names corrupt without it.
• Use DictReader/DictWriter for self-documenting code.
• For European data use delimiter=';' — many spreadsheets use semicolons.
• Large CSVs: stream row by row instead of list(reader).
• For complex analytics, pandas is far more powerful than the built-in module.

• Wrong newline mode: Windows users get blank rows without newline="".
• Manual splitting on commas: fails when fields contain commas inside quotes. Use the csv module.
• Integer vs string: CSV cells are strings — convert with int() / float().
• Loading huge files into memory: use streaming or pandas with chunksize.
• Forgetting header: DictReader needs a header row to map columns.
• Encoding mismatch: opening a UTF-8 CSV as ASCII crashes on non-English text.

• Problem 1: Create a CSV file of 10 students with name, age, marks.
• Problem 2: Read the CSV and print the topper based on marks.
• Problem 3: Append a new student record to an existing CSV.
• Problem 4: Convert a list of dicts (each representing a student) into a CSV using DictWriter.
• Problem 5: Read a CSV and write a new CSV containing only students with marks above 80.
• Problem 6: Use pandas to load a CSV and plot a bar chart of marks by name.

• Q1. Why prefer the csv module over manual string splitting?
• Q2. Difference between csv.reader and csv.DictReader?
• Q3. What is the role of newline="" in open()?
• Q4. How do you handle CSV files where fields contain commas or quotes?
• Q5. How would you process a 5 GB CSV file in Python efficiently?
• Q6. When would you choose pandas over the built-in csv module?

• Q1: Why use the csv module instead of splitting on commas?
  The csv module correctly handles quoted fields, escaped quotes, and embedded commas — manual splitting cannot.
• Q2: Why do I see blank lines on Windows?
  Open the file with newline=''. Without it, Windows adds extra carriage returns.
• Q3: What is DictReader?
  An iterator that returns each row as a dictionary keyed by the header row, making column access by name simple.
• Q4: How do I read a CSV with a custom delimiter?
  Pass delimiter='\t', delimiter=';', or any character via the delimiter argument to csv.reader/writer.
• Q5: Is csv module faster than pandas?
  For pure I/O, comparable. Pandas is faster for analytics because operations are vectorised in C.
• Q6: How do I handle CSVs with millions of rows?
  Stream row by row, use generators, or use pandas with chunksize=10000.