File Handling & Error Handling

# Read:
with open("data.txt", "r") as f:
    content = f.read()

# Write:
with open("output.txt", "w") as f:
    f.write("Hello, World!")

# Append:
with open("log.txt", "a") as f:
    f.write("New log entry\n")

Always use the with statement — it automatically closes the file even if an error occurs. "r" = read, "w" = write (overwrites), "a" = append.

import csv

with open("data.csv", "r") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row["name"], row["salary"])

DictReader gives each row as a dictionary keyed by column name. In practice you will use Pandas for CSV files, but knowing the csv module shows depth of knowledge.

try:
    x = int("not_a_number")
except ValueError as e:
    print(f"Error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")
finally:
    print("This always runs")

try-except prevents a script from crashing on bad data. finally runs cleanup code (close connections, log completion). In data pipelines, wrap file reads and API calls in try-except.

import json

# Read:
with open("data.json", "r") as f:
    data = json.load(f)

# Write:
with open("output.json", "w") as f:
    json.dump(data, f, indent=2)

# String to dict:
d = json.loads('{"name": "Rahul", "age": 28}')

JSON is the standard format for API responses. json.load() reads from a file; json.loads() parses a string. In analytics, you frequently convert JSON API responses into Pandas DataFrames.

import os

os.getcwd()                    # current directory
os.listdir(".")                # list files
os.path.exists("data.csv")    # True/False
os.path.join("data", "sales.csv")  # "data/sales.csv"
os.makedirs("output", exist_ok=True)  # create folder

os.path.join() is critical for cross-platform scripts — never hardcode "/" or "\" separators. exist_ok=True in makedirs prevents errors if the folder already exists.