Working with Files, JSON, CSV, and Dates in Python

Almost every real program needs to read data from somewhere and write results somewhere else. Python’s standard library gives you everything you need to work with text files, JSON APIs, CSV spreadsheets, dates, and file paths — no third-party packages required. This page walks you through each format with practical, copy-paste-ready examples.

Text Files with `open()`

The built-in open() function is your entry point for all file I/O. Always pair it with a with statement — it guarantees the file is closed when the block exits, even if an exception is raised.

File Modes

Mode	Meaning
`"r"`	Read text (default)
`"w"`	Write text — creates or overwrites
`"a"`	Append text — creates or adds to end
`"rb"`	Read binary (images, PDFs, etc.)
`"wb"`	Write binary

Reading

# Read the entire file at once
with open("notes.txt", "r", encoding="utf-8") as f:
    content = f.read()
    print(content)

# Read line-by-line — memory-efficient for large files
with open("server.log", "r") as f:
    for line in f:
        cleaned = line.strip()   # remove trailing \n
        if cleaned:              # skip empty lines
            print(cleaned)

# Read all lines into a list
with open("names.txt", "r") as f:
    names = [line.strip() for line in f if line.strip()]

Writing and Appending

# Write (creates file if needed, overwrites if it exists)
with open("report.txt", "w", encoding="utf-8") as f:
    f.write("Monthly Sales Report\n")
    f.write("====================\n")
    for item in ["Laptop: 50", "Mouse: 200", "Monitor: 30"]:
        f.write(item + "\n")

# Append (adds to existing content)
with open("report.txt", "a") as f:
    f.write("Total: 280 units\n")

Always specify encoding="utf-8" when reading or writing text files that may contain non-ASCII characters (names, addresses, product descriptions). The default encoding varies by operating system.

JSON with the `json` Module

JSON is the lingua franca of web APIs and configuration files. Python’s json module converts seamlessly between Python objects and JSON strings.

The Four Core Functions

Function	Direction
`json.dumps(obj)`	Python object → JSON string
`json.loads(string)`	JSON string → Python object
`json.dump(obj, file)`	Python object → write to file
`json.load(file)`	Read file → Python object

Serialisation and Deserialisation

import json

user = {
    "id":       1001,
    "name":     "Alice Sharma",
    "email":    "[email protected]",
    "skills":   ["Python", "FastAPI", "PostgreSQL"],
    "active":   True,
    "balance":  None
}

# Python dict → JSON string
json_str = json.dumps(user, indent=2)
print(json_str)
# {
#   "id": 1001,
#   "name": "Alice Sharma",
#   ...
# }

# JSON string → Python dict
restored = json.loads(json_str)
print(restored["name"])    # Alice Sharma
print(type(restored["id"])) # <class 'int'>

Reading and Writing JSON Files

import json

# Write to a file
config = {"debug": False, "version": "2.1.0", "max_workers": 4}

with open("config.json", "w") as f:
    json.dump(config, f, indent=2)

# Read back from the file
with open("config.json", "r") as f:
    loaded_config = json.load(f)

print(loaded_config["version"])   # 2.1.0
print(loaded_config["debug"])     # False

JSON ↔ Python Type Mapping

JSON	Python
`object {}`	`dict`
`array []`	`list`
`string`	`str`
`number (int)`	`int`
`number (float)`	`float`
`true` / `false`	`True` / `False`
`null`	`None`

CSV with the `csv` Module

CSV (Comma-Separated Values) is the standard format for spreadsheets and tabular exports. Python’s csv module handles quoting, escaping, and newlines correctly so you don’t have to.

Reading CSV as Rows (Lists)

import csv

with open("employees.csv", "r", newline="") as f:
    reader = csv.reader(f)
    header = next(reader)    # read and skip the header row
    print("Columns:", header)

    for row in reader:
        print(row)           # each row is a list of strings

Reading CSV as Dictionaries (Recommended)

DictReader uses the header row as keys, giving you named access to each field:

import csv

with open("products.csv", "r", newline="") as f:
    reader = csv.DictReader(f)
    for row in reader:
        name  = row["name"]
        price = float(row["price"])   # values are strings — cast as needed
        print(f"{name}: ${price:.2f}")

Writing CSV

import csv

inventory = [
    {"product": "Laptop",   "price": 999.99, "stock": 10},
    {"product": "Mouse",    "price":  29.99, "stock": 50},
    {"product": "Keyboard", "price":  79.99, "stock": 35},
]

with open("inventory.csv", "w", newline="", encoding="utf-8") as f:
    fieldnames = ["product", "price", "stock"]
    writer = csv.DictWriter(f, fieldnames=fieldnames)

    writer.writeheader()       # writes: product,price,stock
    writer.writerows(inventory)

Always open CSV files with newline="". If you don’t, the csv module may mishandle line endings on Windows, producing extra blank rows in your output.

Dates and Times with `datetime`

from datetime import datetime, date, timedelta

# Current date and time
now   = datetime.now()
today = date.today()
print(now)    # 2024-06-15 14:32:07.123456
print(today)  # 2024-06-15

# Parse a date string
birthday = datetime.strptime("1990-03-22", "%Y-%m-%d")
print(birthday.year)    # 1990

# Format a datetime as a string
formatted = now.strftime("%d %B %Y at %H:%M")
print(formatted)        # 15 June 2024 at 14:32

# Arithmetic with timedelta
next_week   = today + timedelta(weeks=1)
thirty_days = today + timedelta(days=30)
age_days    = (today - birthday.date()).days
print(f"Age in days: {age_days:,}")

Modern Path Handling with `pathlib`

pathlib.Path is the modern, object-oriented way to work with file system paths. It replaces most uses of os.path with a much cleaner API.

from pathlib import Path

# Create Path objects
base_dir = Path("data")
config   = base_dir / "config.json"     # / operator joins paths
logs     = base_dir / "logs" / "app.log"

# Inspect paths
print(config.name)          # config.json
print(config.stem)          # config
print(config.suffix)        # .json
print(config.parent)        # data
print(config.exists())      # True / False

# Create directories
(base_dir / "output").mkdir(parents=True, exist_ok=True)

# Read and write files via Path
config.write_text('{"debug": false}')
content = config.read_text()

# List files in a directory
for path in Path("data").iterdir():
    print(path)

# Glob for specific files
for json_file in Path(".").glob("**/*.json"):
    print(json_file)

Practical Example: Read a Config + Process a CSV

Here’s a complete workflow that ties everything together — load a JSON config, then process a CSV data file respecting those config settings:

import json
import csv
from pathlib import Path
from datetime import datetime

# ── Config ─────────────────────────────────────────────────────────────────
config_path = Path("config.json")
config_path.write_text(json.dumps({
    "min_price": 50.0,
    "currency":  "USD",
    "report_date": "2024-06-15"
}))

with config_path.open() as f:
    config = json.load(f)

min_price   = config["min_price"]
currency    = config["currency"]
report_date = datetime.strptime(config["report_date"], "%Y-%m-%d")

# ── Data ───────────────────────────────────────────────────────────────────
data_path = Path("products.csv")
data_path.write_text(
    "name,price,stock\n"
    "Laptop,999.99,5\n"
    "Mouse,29.99,50\n"
    "Keyboard,79.99,20\n"
    "Cable,9.99,100\n"
)

# ── Process ────────────────────────────────────────────────────────────────
results = []

with data_path.open(newline="") as f:
    for row in csv.DictReader(f):
        price = float(row["price"])
        if price >= min_price:
            results.append({
                "name":  row["name"],
                "price": price,
                "stock": int(row["stock"]),
            })

# ── Report ─────────────────────────────────────────────────────────────────
print(f"Report for {report_date.strftime('%d %B %Y')}")
print(f"Products priced ≥ {currency} {min_price:.2f}:\n")

for item in sorted(results, key=lambda x: x["price"], reverse=True):
    total = item["price"] * item["stock"]
    print(f"  {item['name']:12s}  {currency} {item['price']:>8.2f}  "
          f"(stock: {item['stock']}, value: {currency} {total:,.2f})")

# Report for 15 June 2024
# Products priced ≥ USD 50.00:
#
#   Laptop       USD   999.99  (stock: 5, value: USD 4,999.95)
#   Keyboard     USD    79.99  (stock: 20, value: USD 1,599.80)

Core Python

Advanced Python

Pydantic

Working with Files, JSON, CSV, and Dates in Python

Text Files with `open()`

File Modes

Reading

Writing and Appending

JSON with the `json` Module

The Four Core Functions

Serialisation and Deserialisation

Reading and Writing JSON Files

JSON ↔ Python Type Mapping

CSV with the `csv` Module

Reading CSV as Rows (Lists)

Reading CSV as Dictionaries (Recommended)

Writing CSV

Dates and Times with `datetime`

Modern Path Handling with `pathlib`

Practical Example: Read a Config + Process a CSV

​Text Files with open()

​File Modes

​Reading

​Writing and Appending

​JSON with the json Module

​The Four Core Functions

​Serialisation and Deserialisation

​Reading and Writing JSON Files

​JSON ↔ Python Type Mapping

​CSV with the csv Module

​Reading CSV as Rows (Lists)

​Reading CSV as Dictionaries (Recommended)

​Writing CSV

​Dates and Times with datetime

​Modern Path Handling with pathlib

​Practical Example: Read a Config + Process a CSV

Text Files with `open()`

File Modes

Reading

Writing and Appending

JSON with the `json` Module

The Four Core Functions

Serialisation and Deserialisation

Reading and Writing JSON Files

JSON ↔ Python Type Mapping

CSV with the `csv` Module

Reading CSV as Rows (Lists)

Reading CSV as Dictionaries (Recommended)

Writing CSV

Dates and Times with `datetime`

Modern Path Handling with `pathlib`

Practical Example: Read a Config + Process a CSV