Real-world data analytics almost always starts with a file — a CSV export from Excel, a JSON response from an API, or a plain text log. File handling lets Python read data from and write data to these files on disk.
with open("students.txt", "r") as file:
content = file.read()
print(content)
# Reading line by line
with open("students.txt", "r") as file:
for line in file:
print(line.strip())| Mode | Meaning |
|---|---|
| 'r' | Read (default) — file must already exist |
| 'w' | Write — creates a new file or overwrites an existing one |
| 'a' | Append — adds to the end of an existing file |
| 'r+' | Read and write |
Writing mode ('w') creates a new file or completely overwrites an existing one — use this mode carefully!
with open("enrolled_students.txt", "w") as file:
file.write("Priya Sharma\n")
file.write("Rahul Verma\n")
file.write("Anita Desai\n")
print("File written successfully!")with open("enrolled_students.txt", "a") as file:
file.write("Vikram Singh\n")
print("New student added without deleting existing data!")CSV (Comma-Separated Values) is the most common data file format in analytics — Excel exports, reports, and datasets are almost always shared as CSV files.
import csv
with open("students.csv", "r") as file:
reader = csv.reader(file)
for row in reader:
print(row)import csv
data = [
["Name", "Course", "Fee"],
["Priya", "SEO", 12000],
["Rahul", "Google Ads", 15000],
]
with open("new_students.csv", "w", newline="") as file:
writer = csv.writer(file)
writer.writerows(data)JSON (JavaScript Object Notation) is the standard format for APIs and structured configuration data — it looks almost identical to a Python dictionary.
import json
student = {"name": "Priya", "course": "SEO", "fee": 12000}
# Writing JSON to a file
with open("student.json", "w") as file:
json.dump(student, file, indent=4)
# Reading JSON from a file
with open("student.json", "r") as file:
data = json.load(file)
print(data["name"])import csv
total_fee = 0
with open("students.csv", "r") as file:
reader = csv.DictReader(file)
for row in reader:
total_fee += int(row["Fee"])
print("Total Fee Collected:", total_fee)Basic (5 Questions)
1. Create a text file and write your name and course into it.
2. Read and print the contents of the file you just created.
3. Append one more line to the same file without deleting existing content.
4. Write a small CSV file with 3 students' names and marks.
5. Read the CSV file you created and print each row.
Intermediate (5 Questions)
1. Write a program that counts the number of lines in a text file.
2. Write a program that saves a Python dictionary of student data to a JSON file.
3. Write a program that reads a JSON file back and prints specific fields.
4. Write a CSV file with headers, then read it back using csv.DictReader.
5. Handle the FileNotFoundError gracefully when trying to read a file that doesn't exist.
Advanced (5 Questions)
1. Build a program that reads a CSV of student fees and calculates the total, average, and highest fee.
2. Write a program that reads a text file of student names (one per line) and writes only names starting with 'A' into a new file.
3. Build a simple JSON-based 'Student Database': add a student, save to JSON, and reload it to confirm the data persisted.
4. Write a program that merges two CSV files of student records into a single combined CSV.
5. Build a 'Log Writer' that appends a timestamped entry to a log file every time a function is called.
Q1. Which file mode overwrites an existing file?
Q2. Which mode adds data without deleting existing content?
Q3. Which module is used to read/write CSV files?
Q4. What does json.load() do?
Q5. What is the safest way to open a file in Python?
Assignment: Student Fee Report Generator
Expected Output: A working pipeline that reads raw CSV data, computes summary statistics, and saves + reloads them as a structured JSON report.
Use this space to write down key points, doubts, and your own examples from today's session.