JSON and CSV Formats in Python: Reading, Writing, and Data Analysis

We've already covered the basics of working with files and text files in Python. Now we're moving on to structured data — formats that are used to store and exchange data in a structured way. 🧩

We'll focus on two of the most popular formats:

• JSON (JavaScript Object Notation) — a data exchange format widely used in web applications and APIs
• CSV (Comma-Separated Values) — a simple format for representing tabular data

These formats are very common and are used in all areas of programming — from web development to data analysis.

JSON: JavaScript Object Notation

JSON (JavaScript Object Notation) is a text-based data exchange format, similar to dictionaries and lists in Python. It is easy to read by both humans and machines.

JSON Data Structure

JSON supports the following data types:

• Objects (dictionaries): {"name": "Alice", "age": 30}
• Arrays (lists): [1, 2, 3, 4]
• Strings: "Hello, world!"
• Numbers: 42, 3.14
• Boolean values: true, false
• null (corresponds to None in Python)

The json Module in Python

Python provides a built-in json module for working with this format:

Python 3.13
>>> import json

# Simple Python dictionary
>>> person = {
...     "name": "Anna",
...     "age": 28,
...     "city": "Moscow",
...     "languages": ["Python", "JavaScript"]
... }

# Converting a Python dictionary to a JSON string
>>> json_string = json.dumps(person, ensure_ascii=False, indent=2)
>>> print("JSON string:")
>>> print(json_string)
JSON string:
{
  "name": "Anna",
  "age": 28,
  "city": "Moscow",
  "languages": [
    "Python",
    "JavaScript"
  ]
}

# Converting a JSON string back to a Python object
>>> parsed_data = json.loads(json_string)
>>> print("\nConverted back to Python:")
>>> print(f"Name: {parsed_data['name']}")
>>> print(f"Age: {parsed_data['age']}")
>>> print(f"Programming languages: {', '.join(parsed_data['languages'])}")

Converted back to Python:
Name: Anna
Age: 28
Programming languages: Python, JavaScript

Writing JSON to a File and Reading from a File

Here's how to save data to a JSON file and then read it:

Python 3.13
>>> import json

# Student data
>>> students = [
...     {"id": 1, "name": "Ivan", "scores": [85, 90, 78]},
...     {"id": 2, "name": "Maria", "scores": [92, 88, 95]}
... ]

# Writing to a file
>>> with open('students.json', 'w', encoding='utf-8') as file:
...     json.dump(students, file, ensure_ascii=False, indent=2)
...     print("Data written to students.json file")
Data written to students.json file

# Reading from a file
>>> with open('students.json', 'r', encoding='utf-8') as file:
...     loaded_students = json.load(file)
...     print(f"\nLoaded {len(loaded_students)} students:")

>>>     for student in loaded_students:
...         avg_score = sum(student['scores']) / len(student['scores'])
...         print(f"  {student['name']}: average score {avg_score:.1f}")

Loaded 2 students:
  Ivan: average score 84.3
  Maria: average score 91.7

Main Methods of the json Module

The ensure_ascii=False parameter allows correct saving of non-ASCII characters and other Unicode symbols, and indent makes the output more readable.

CSV: Comma-Separated Values

CSV (Comma-Separated Values) is a simple text format for representing tabular data, where table rows are file lines, and columns are separated by commas (or other delimiters).

CSV looks something like this:

Python 3.13
Name,Age,City
Anna,28,Moscow
Ivan,35,Saint Petersburg

The csv Module in Python

Python provides a built-in csv module for working with this format:

Python 3.13
>>> import csv

# Data to write
>>> data = [
...     ['Name', 'Age', 'City'],  # Headers
...     ['Anna', '28', 'Moscow'],
...     ['Ivan', '35', 'Saint Petersburg'],
...     ['Maria', '22', 'Kazan']
... ]

# Writing to a CSV file
>>> with open('people.csv', 'w', newline='', encoding='utf-8') as file:
...     writer = csv.writer(file)
...     writer.writerows(data)
...     print("Data written to people.csv file")
Data written to people.csv file

# Reading from a CSV file
>>> with open('people.csv', 'r', encoding='utf-8') as file:
...     reader = csv.reader(file)

>>>     # Reading headers (first line)
...     headers = next(reader)
...     print(f"\nHeaders: {headers}")

>>>     # Reading data
...     print("\nData:")
...     for row in reader:
...         print(f"  {row[0]}, {row[1]} years old, city {row[2]}")

Headers: ['Name', 'Age', 'City']

Data:
  Anna, 28 years old, city Moscow
  Ivan, 35 years old, city Saint Petersburg
  Maria, 22 years old, city Kazan

Using DictReader and DictWriter

For more convenient work with CSV, you can use DictReader and DictWriter, which allow you to work with data as dictionaries:

Python 3.13
>>> import csv

# Writing to CSV using DictWriter
>>> data = [
...     {'Name': 'Alex', 'Profession': 'Engineer', 'Salary': 85000},
...     {'Name': 'Kate', 'Profession': 'Designer', 'Salary': 75000},
...     {'Name': 'Sergey', 'Profession': 'Programmer', 'Salary': 110000}
... ]

>>> with open('employees.csv', 'w', newline='', encoding='utf-8') as file:
...     fieldnames = ['Name', 'Profession', 'Salary']
...     writer = csv.DictWriter(file, fieldnames=fieldnames)

>>>     writer.writeheader()  # Writing headers
...     writer.writerows(data)  # Writing data
...     print("Employee data written to file")
Employee data written to file

# Reading from CSV using DictReader
>>> with open('employees.csv', 'r', encoding='utf-8') as file:
...     reader = csv.DictReader(file)

>>>     print("\nEmployees:")
...     for row in reader:
...         print(f"  {row['Name']} - {row['Profession']}, salary: {row['Salary']} units")

Employees:
  Alex - Engineer, salary: 85000 units
  Kate - Designer, salary: 75000 units
  Sergey - Programmer, salary: 110000 units

Main Features of Working with CSV

1. Delimiters: Although CSV stands for "Comma-Separated Values", in practice other delimiters (semicolon, tab) can be used
2. Quotes: If a value contains a delimiter or quotes, it is enclosed in quotes
3. Escaping: If there are quotes inside a value, they are escaped

Python 3.13
>>> import csv

# Example with a different delimiter
>>> data = [
...     ['Product', 'Price', 'In Stock'],
...     ['Laptop', '45000', 'Yes'],
...     ['Smartphone', '25000', 'No']
... ]

# Writing using semicolon
>>> with open('products.csv', 'w', newline='', encoding='utf-8') as file:
...     writer = csv.writer(file, delimiter=';')
...     writer.writerows(data)
...     print("Data written with delimiter ';'")
Data written with delimiter ';'

# Reading with the correct delimiter
>>> with open('products.csv', 'r', encoding='utf-8') as file:
...     reader = csv.reader(file, delimiter=';')
...     for row in reader:
...         print('  '.join(row))
Product  Price  In Stock
Laptop  45000  Yes
Smartphone  25000  No

Practical Example: Sales Data Analysis

Let's consider an example where we first save sales data in CSV, then analyze it and save the results in JSON:

Python 3.13
>>> import csv
>>> import json

# Creating sales data
>>> sales = [
...     ['Date', 'Product', 'Category', 'Price', 'Quantity'],
...     ['2023-01-05', 'HP Laptop', 'Electronics', '45000', '2'],
...     ['2023-01-10', 'Apple Smartphone', 'Electronics', '85000', '3'],
...     ['2023-01-15', 'Book "Python"', 'Books', '1200', '5'],
...     ['2023-02-10', 'Microwave', 'Home Appliances', '7000', '1']
... ]

# Step 1: Save data to CSV
>>> with open('sales.csv', 'w', newline='', encoding='utf-8') as file:
...     writer = csv.writer(file)
...     writer.writerows(sales)
...     print("Sales data saved to CSV")
Sales data saved to CSV

# Step 2: Read and analyze data
>>> with open('sales.csv', 'r', encoding='utf-8') as file:
...     reader = csv.reader(file)
...     headers = next(reader)  # Skip headers

>>>     # Preparing variables for analysis
...     total_revenue = 0
...     sales_by_category = {}

>>>     # Data analysis
...     for row in reader:
...         date, product, category, price, quantity = row
...         revenue = float(price) * int(quantity)

>>>         # Total revenue
...         total_revenue += revenue

>>>         # Revenue by category
...         if category in sales_by_category:
...             sales_by_category[category] += revenue
...         else:
...             sales_by_category[category] = revenue

>>>     # Output analysis results
...     print(f"\nTotal revenue: {total_revenue} units")
...     print("\nRevenue by category:")
...     for category, rev in sales_by_category.items():
...         print(f"  {category}: {rev} units")

Total revenue: 354000.0 units

Revenue by category:
  Electronics: 345000.0 units
  Books: 6000.0 units
  Home Appliances: 7000.0 units

# Step 3: Save analysis results to JSON
>>> results = {
...     "total_revenue": total_revenue,
...     "sales_by_category": sales_by_category
... }

>>> with open('sales_analysis.json', 'w', encoding='utf-8') as file:
...     json.dump(results, file, ensure_ascii=False, indent=2)
...     print("\nAnalysis results saved to JSON")

Analysis results saved to JSON

# Step 4: Check saved JSON
>>> with open('sales_analysis.json', 'r', encoding='utf-8') as file:
...     saved_results = json.load(file)
...     print("\nContents of the JSON file with results:")
...     print(json.dumps(saved_results, ensure_ascii=False, indent=2))

Contents of the JSON file with results:
{
  "total_revenue": 354000.0,
  "sales_by_category": {
    "Electronics": 345000.0,
    "Books": 6000.0,
    "Home Appliances": 7000.0
  }
}

In this example we:

1. Created a CSV file with sales data
2. Read the data and calculated revenue by category
3. Saved the analysis results to a JSON file
4. Read the saved JSON to ensure correctness

Understanding Check

Which code correctly reads data from a JSON file in Python?