Working with Data Files: Text, JSON & CSV in Python

Read, write, and process plain text files, JSON API payloads, and CSV spreadsheets using only Python’s built-in standard library tools.

Programs exist to process data. Whether you’re loading a configuration file, consuming a REST API, or analysing a spreadsheet, Python gives you everything you need in the standard library — no extra packages required. This page covers three of the most common data formats you’ll encounter: plain text (.txt), JSON (the universal API format), and CSV (tabular/spreadsheet data). You’ll learn to read, manipulate, and save each format using clean, Pythonic patterns.

Handling Text Data

Plain text files are the simplest format. Python provides the built-in open() function to read and write them.

Reading Text Files

Always use the with statement — it automatically closes the file even if an exception occurs:

# Read the entire file at once
with open("sample.txt", "r") as file:
    content = file.read()
    print(content)

# Read line-by-line (memory-efficient for large files)
with open("sample.txt", "r") as file:
    for line in file:
        print(line.strip())   # .strip() removes trailing newlines

Writing Text Files

Use "w" to overwrite the file (creates it if it doesn’t exist) or "a" to append:

with open("output.txt", "w") as file:
    file.write("Hello, World!\n")
    file.write("Writing data is easy in Python.")

If you open a file with "w" and it already exists, its contents are erased before writing. Use "a" when you want to add to the end of an existing file.

Handling JSON Data

JSON (JavaScript Object Notation) is the standard format for web APIs and configuration files. Python’s built-in json module handles serialisation (Python → JSON string) and deserialisation (JSON string → Python).

JSON ↔ Python Type Mapping

JSON	Python
Object {}	dict
Array []	list
String	str
Number	int / float
true / false	True / False
null	None

Core Functions

Function	Purpose
json.loads(s)	JSON string → Python object
json.dumps(obj)	Python object → JSON string
json.load(f)	Read JSON from a file object
json.dump(obj, f)	Write JSON to a file object

JSON in Practice

import json

user_profile = {
    "name": "Alice",
    "age": 25,
    "skills": ["Python", "Machine Learning"],
    "is_active": True
}

# 1. Serialise (Dict → JSON string)
json_string = json.dumps(user_profile, indent=4)
print(json_string)

# 2. Deserialise (JSON string → Dict)
data_dict = json.loads(json_string)
print(data_dict["name"])     # Alice

# 3. Write JSON to a file
with open("profile.json", "w") as file:
    json.dump(user_profile, file, indent=4)

# 4. Read JSON from a file
with open("profile.json", "r") as file:
    loaded_data = json.load(file)
    print(loaded_data["skills"])   # ['Python', 'Machine Learning']

Use indent=4 in json.dumps() and json.dump() to produce human-readable, pretty-printed JSON. For compact machine-to-machine transfer, omit the indent argument.

Handling CSV Data

CSV (Comma-Separated Values) is the standard format for spreadsheets and tabular data. Python’s csv module provides both simple list-based and dictionary-based readers and writers.

Reading CSV Files

As lists (one row = one list of strings):

import csv

with open("employees.csv", "r") as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)   # ['Name', 'Age', 'Role']

As dictionaries — recommended because column names become keys:

import csv

with open("employees.csv", "r") as file:
    dict_reader = csv.DictReader(file)
    for row in dict_reader:
        print(f"Name: {row['Name']}, Role: {row['Role']}")

Writing CSV Files

import csv

fieldnames = ["Product", "Price", "Stock"]
products = [
    {"Product": "Laptop", "Price": 999.99, "Stock": 10},
    {"Product": "Mouse",  "Price": 29.99,  "Stock": 50}
]

with open("inventory.csv", "w", newline="") as file:
    writer = csv.DictWriter(file, fieldnames=fieldnames)
    writer.writeheader()     # writes the column names row
    writer.writerows(products)

Always pass newline="" when opening a CSV file for writing on Windows to prevent extra blank lines from appearing between rows.

Choosing the Right Format

Use case	Format
Configuration or human-readable logs	Text (.txt)
REST API payloads and responses	JSON
Spreadsheets, data exports, reports	CSV
Structured data with validation	JSON + Pydantic