Working with JSON and CSV

In this lesson, we will explore how to handle JSON and CSV data in Python. Let’s start with some core facts verified against the official Python 3.14 documentation.

Fact 1

Claim: The json.dumps() function serializes a Python object to a JSON formatted string.

json.dumps() turns a Python object into a JSON string — that trailing s is the whole difference from dump(), which writes straight to a file.

Example: Serializing a Python dictionary to a JSON string

import json
data = {'name': 'Alice', 'age': 30}
json_str = json.dumps(data)
print(json_str)

{"name": "Alice", "age": 30}

Fact 2

Claim: The json.loads() function deserializes a JSON string into a Python object.

json.loads() reads the other direction, parsing a JSON string back into Python dicts and lists. Same rule: loads takes a string, load takes an open file.

Example: Deserializing a JSON string to a Python dictionary

import json
json_str = '{"status": "ok", "count": 5}'
data = json.loads(json_str)
print(data['status'])
print(data['count'])

ok
5

Fact 3

Claim: The csv.reader returns an object that iterates over lines in the given CSV file.

csv.reader() hands you an iterator over the rows of a file, each row a list of strings. It handles the awkward parts of CSV — quoted fields, embedded commas — that break naive line.split(',').

Example: Reading delimited data using csv.reader

import csv
csv_data = ['a,b,c', '1,2,3', '4,5,6']
reader = csv.reader(csv_data)
for row in reader:
    print(row)

['a', 'b', 'c']
['1', '2', '3']
['4', '5', '6']

Fact 4

Claim: The csv.writer returns an object responsible for converting data into delimited strings.

csv.writer() does the reverse, quoting and escaping fields as needed. Open the file with newline='', or you will get a blank line between every row on Windows.

Example: Writing lists to delimited string using csv.writer

import csv
import io
output = io.StringIO()
writer = csv.writer(output)
writer.writerow(['id', 'value'])
writer.writerow([1, 100])
print(output.getvalue(), end='')

id,value
1,100

Fact 5

Claim: The csv.DictReader maps the information in each row to a dictionary.

csv.DictReader is usually the one you want: it reads the header row and gives each subsequent row back as a dict, so you write row['email'] instead of row[3] and stop caring about column order.

Example: Reading CSV rows as dictionaries using csv.DictReader

import csv
csv_data = ['name,age', 'Bob,25', 'Charlie,40']
reader = csv.DictReader(csv_data)
for row in reader:
    print(row['name'], row['age'])

Bob 25
Charlie 40

Edge Cases to Consider

• Parsing CSV files where fields contain commas surrounded by quotes.
• Serializing Python objects that have circular references or unsupported types (like datetime) to JSON.

Conceptual Overview

import json

# From string to dict (loads)
json_str = '{"a": 1}'
parsed_dict = json.loads(json_str)
print('Parsed:', parsed_dict)

# From dict to string (dumps)
new_str = json.dumps(parsed_dict)
print('Stringified:', new_str)