DEV Community: Rahul Gupta

Day 45/100 – Reading/Writing JSON in Python

Rahul Gupta — Wed, 13 Aug 2025 03:47:55 +0000

Welcome to Day 45 of the 100 Days of Python series!
Today, we’ll explore how to work with JSON (JavaScript Object Notation) in Python — a common data format used in APIs, configuration files, and data exchange between systems.

1. What is JSON?

JSON is a lightweight data-interchange format.
It is human-readable and language-independent.
It uses a key-value pair structure similar to Python dictionaries.

Example JSON:

{
    "name": "Alice",
    "age": 25,
    "skills": ["Python", "Data Science"]
}

2. Python’s `json` Module

Python provides a built-in json module to:

Parse JSON into Python objects.
Convert Python objects into JSON strings.

import json

3. Reading JSON (Deserialization)

To read JSON from a file and convert it into a Python object:

import json

with open("data.json", "r") as file:
    data = json.load(file)

print(data)
print(type(data))  # dict

Key Method:

json.load(file) → Reads JSON from a file and converts it to a Python object.

4. Writing JSON (Serialization)

To write Python objects to a JSON file:

data = {
    "name": "Alice",
    "age": 25,
    "skills": ["Python", "Data Science"]
}

with open("data.json", "w") as file:
    json.dump(data, file, indent=4)

Key Method:

json.dump(obj, file, indent=4) → Writes Python objects to a file in JSON format.
indent makes it human-readable.

5. Converting JSON Strings

Sometimes you work with JSON from APIs, which comes as a string.

String to Python Object:

json_string = '{"name": "Bob", "age": 30}'
data = json.loads(json_string)
print(data["name"])  # Bob

Python Object to JSON String:

data = {"name": "Charlie", "age": 22}
json_str = json.dumps(data, indent=2)
print(json_str)

6. Common Pitfalls

JSON keys must be strings.
JSON cannot store Python-specific objects like sets or functions directly.
Use default=str when serializing objects like datetime.

7. Mini Example

import json

user = {
    "username": "tech_guru",
    "active": True,
    "followers": 1050
}

# Save to file
with open("user.json", "w") as f:
    json.dump(user, f, indent=4)

# Read from file
with open("user.json", "r") as f:
    loaded_user = json.load(f)

print(loaded_user)

Notes

Serialization = Python → JSON (dump, dumps)
Deserialization = JSON → Python (load, loads)
Always handle file operations with with to ensure proper closure.

Challenge

Create a Python program that:

Reads user details (name, age, skills) from input().
Saves them to a JSON file.
Reads the file and prints the stored details.

Day 44/100 – Working with CSV Files in Python

Rahul Gupta — Wed, 13 Aug 2025 00:59:34 +0000

Welcome back to 100 Days, 100 Python Topics! Yesterday, we explored file modes and error handling. Today, we’ll focus on CSV files — a very common format for storing tabular data. Whether you’re dealing with spreadsheets, datasets, or exporting data from an app, Python’s csv module makes working with CSV files a breeze.

1. What is a CSV File?

A CSV (Comma-Separated Values) file is a plain text file where each line represents a row of data, and each value in the row is separated by a comma (or sometimes other delimiters like ; or \t).

Example:

Name,Age,Country
John,25,USA
Emma,30,UK
Liam,28,Canada

2. Reading CSV Files

Python provides the built-in csv module to read CSV files easily.

import csv

# Reading a CSV file
with open('data.csv', mode='r') as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

Notes:

csv.reader() returns each row as a list of strings.
The first row often contains headers, which you might want to skip using next(reader).

Skipping the header example:

with open('data.csv', 'r') as file:
    reader = csv.reader(file)
    next(reader)  # Skip the first row
    for row in reader:
        print(row)

3. Writing to CSV Files

To create or modify a CSV file, use csv.writer().

import csv

# Writing to a CSV file
data = [
    ["Name", "Age", "Country"],
    ["John", 25, "USA"],
    ["Emma", 30, "UK"]
]

with open('output.csv', mode='w', newline='') as file:
    writer = csv.writer(file)
    writer.writerows(data)

Key points:

newline='' prevents adding extra blank lines on some systems.
Use writer.writerow() for a single row, writer.writerows() for multiple rows.

4. Using DictReader and DictWriter

Instead of working with lists, DictReader and DictWriter let you use column headers as keys.

Reading CSV as dictionaries:

import csv

with open('data.csv', mode='r') as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row['Name'], row['Country'])

Writing CSV from dictionaries:

import csv

data = [
    {"Name": "John", "Age": 25, "Country": "USA"},
    {"Name": "Emma", "Age": 30, "Country": "UK"}
]

with open('dict_output.csv', mode='w', newline='') as file:
    fieldnames = ["Name", "Age", "Country"]
    writer = csv.DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    writer.writerows(data)

5. Handling Different Delimiters

Not all CSVs use commas. Some use semicolons or tabs.

import csv

with open('semicolon_data.csv', mode='r') as file:
    reader = csv.reader(file, delimiter=';')
    for row in reader:
        print(row)

6. Best Practices

✅ Always use with open() for automatic file closing.
✅ Use newline='' when writing to avoid extra blank lines.
✅ Consider pandas for large datasets.
✅ Handle encoding (e.g., encoding='utf-8') if your CSV contains special characters.

Challenge for You

Create a CSV file containing at least 5 rows of employee data.
Write a script to:

Read the CSV file using DictReader.
Add a new column for "Salary".
Save it back to a new CSV file using DictWriter.

Day 43/100: File Modes and Error Handling in Python

Rahul Gupta — Wed, 13 Aug 2025 00:56:56 +0000

Welcome back to 100 Days of Python!
In the last few days, we explored reading and writing files.
Today, we’ll dig deeper into file modes (how Python opens files) and error handling to make your file operations safe and reliable.

📄 1. File Modes Overview

When you use open() in Python, you specify a mode to tell Python how to open the file.

Mode	Meaning	Creates File?	Overwrites?
`"r"`	Read (default)	❌	❌
`"w"`	Write	✅	✅
`"a"`	Append	✅	❌
`"x"`	Create (fails if exists)	✅	❌
`"b"`	Binary mode	—	—
`"t"`	Text mode (default)	—	—
`"+"`	Read and write	—	—

Examples:

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

# Write mode
with open("output.txt", "w") as f:
    f.write("Overwrites content!")

# Append mode
with open("output.txt", "a") as f:
    f.write("\nNew line added.")

# Binary mode
with open("image.png", "rb") as f:
    binary_data = f.read()

🛡️ 2. Error Handling in File Operations

File operations can fail for many reasons:

File doesn’t exist
No permission to read/write
Disk full or path invalid

To prevent crashes, use try-except blocks:

try:
    with open("data.txt", "r") as f:
        content = f.read()
except FileNotFoundError:
    print("File not found! Please check the filename.")
except PermissionError:
    print("You don't have permission to access this file.")
except Exception as e:
    print(f"Unexpected error: {e}")

⚡ 3. Combining Modes and Exception Handling

Example: Reading and Writing Safely

filename = "notes.txt"

try:
    with open(filename, "a+") as f:  # Append & read
        f.write("New note added.\n")
        f.seek(0)  # Go to start to read file
        print(f.read())
except IOError as e:
    print(f"I/O error occurred: {e}")

🧠 4. Tips for Working with File Modes

Default mode is "r" (read text).
Use "b" for binary files like images or PDFs.
"a" is safer than "w" if you don’t want to lose existing data.
Always handle errors to prevent program crashes.
Use with open() to automatically close files.

🛠️ Practice Challenge

Write a script that:

Opens a file in "x" mode and writes "First entry".
If the file already exists, appends "Another entry" instead.
Handles all file-related errors gracefully.

Day 42/100: Writing Files and Using Context Managers in Python

Rahul Gupta — Fri, 01 Aug 2025 14:04:53 +0000

Welcome to Day 42 of your Python journey!
Yesterday, we explored reading files using open(). Today, we’ll move on to writing and appending files, while also mastering context managers to handle files safely.

📥 1. Writing Files with `open()`

To create or overwrite a file, use the "w" mode:

# Creates a new file or overwrites existing one
with open("output.txt", "w") as f:
    f.write("Hello, World!\n")
    f.write("This will overwrite the file content.\n")

✅ Note: "w" erases all existing content before writing.

📄 2. Appending to Files

To add content without deleting existing data, use "a" mode:

with open("output.txt", "a") as f:
    f.write("Appending a new line.\n")

✅ "a" keeps the old data and adds new content at the end.

🔁 3. Writing Multiple Lines

You can write a list of strings with writelines():

lines = ["Line 1\n", "Line 2\n", "Line 3\n"]
with open("output.txt", "w") as f:
    f.writelines(lines)

🧠 4. Using Context Managers (`with`)

You might notice we’re using with open(...) as f: everywhere. This is a context manager.

Why use `with`?

Automatically closes the file.
Prevents resource leaks.
Handles errors gracefully.

with open("example.txt", "w") as f:
    f.write("Safe and clean file handling!")

No need to call f.close() — Python handles it for you.

📌 5. Writing Different Data Types

You can only write strings to files. For other types, convert them:

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

with open("data.txt", "w") as f:
    f.write(str(data))

For structured data, consider using JSON (Day 36 covered this).

🔄 6. Handling Errors Safely

Always handle exceptions when dealing with file writing:

try:
    with open("output.txt", "w") as f:
        f.write("Important data")
except IOError:
    print("Error writing to file!")

🛠️ Real-World Example: Logging System

from datetime import datetime

def log_event(message):
    with open("log.txt", "a") as f:
        f.write(f"[{datetime.now()}] {message}\n")

log_event("User logged in")
log_event("User uploaded a file")

📜 Summary

"w" = Write (creates/overwrites file)
"a" = Append (adds to existing file)
Always use with open() to manage resources automatically.
Convert non-string data before writing.
Use error handling to avoid crashes during file operations.

🧪 Practice Challenge

Create a program that writes user input to a file until they type "exit".
Build a simple logger that writes timestamps and messages to a file.
Write a list of names to a file, one per line, and then append more names later.

Day 41/100: Reading Files with open() in Python

Rahul Gupta — Fri, 01 Aug 2025 14:00:43 +0000

Welcome to Day 41 of your Python journey!
Working with files is a core skill for any developer. Whether you're handling logs, reading configurations, or processing data, Python’s built-in open() function makes it simple and powerful.

Today, we’ll learn how to read files safely and efficiently using open().

📥 1. The `open()` Function Basics

The syntax is:

file = open("filename.txt", "mode")

"r" – Read mode (default)
"w" – Write mode (overwrites file)
"a" – Append mode
"b" – Binary mode (use with images, etc.)
"t" – Text mode (default)

Always remember to close files when done:

file = open("sample.txt", "r")
content = file.read()
file.close()

📄 2. Reading the Whole File

with open("sample.txt", "r") as f:
    data = f.read()
    print(data)

✅ Using with automatically closes the file, even if an error occurs.

📜 3. Reading Line by Line

`readline()` – Reads one line at a time:

with open("sample.txt", "r") as f:
    line = f.readline()
    while line:
        print(line.strip())
        line = f.readline()

`readlines()` – Returns all lines as a list:

with open("sample.txt", "r") as f:
    lines = f.readlines()
    print(lines)

📏 4. Reading Fixed Amount of Characters

with open("sample.txt", "r") as f:
    first_10_chars = f.read(10)
    print(first_10_chars)

🔄 5. Iterating Over a File Object

You can loop through a file directly:

with open("sample.txt", "r") as f:
    for line in f:
        print(line.strip())

This is memory-efficient for large files because it reads line by line.

🧨 6. Handling Errors Safely

Always handle exceptions when dealing with files:

try:
    with open("non_existent.txt", "r") as f:
        data = f.read()
except FileNotFoundError:
    print("File not found!")
except IOError:
    print("Error reading file!")

🛠️ Real-World Example: Count Words in a File

with open("sample.txt", "r") as f:
    text = f.read()
    words = text.split()
    print("Total Words:", len(words))

📌 Summary

Use open(filename, "r") to read files.
Always close files or use with open() for safety.
read(), readline(), and readlines() give you flexibility.
Handle errors with try-except.
Iterating over file objects is memory-efficient for large files.

🧪 Practice Challenge

Read a file and count:

Total lines
Total words
Total characters
1. Write a script to read a large log file and only print lines containing the word "ERROR".
2. Read a file and reverse the order of its lines before printing.

Day 40/100: Python math, random, and statistics Modules

Rahul Gupta — Fri, 01 Aug 2025 13:58:50 +0000

Welcome to Day 40 of your Python journey!
Today, we’ll explore three powerful standard library modules for mathematics, randomness, and data analysis:

math – advanced mathematical operations
random – generating random numbers and selections
statistics – quick stats for numerical data

These are essential for everything from games and simulations to analytics and scientific computing.

🧮 1. The `math` Module

Import it first:

import math

✅ Common Functions:

print(math.sqrt(16))       # 4.0
print(math.factorial(5))   # 120
print(math.pow(2, 3))      # 8.0
print(math.ceil(4.3))      # 5
print(math.floor(4.7))     # 4

✅ Constants:

print(math.pi)    # 3.141592653589793
print(math.e)     # 2.718281828459045

✅ Trigonometry:

print(math.sin(math.radians(30)))  # 0.5
print(math.cos(math.radians(60)))  # 0.5
print(math.tan(math.radians(45)))  # 1.0

🎲 2. The `random` Module

For generating random numbers and making selections:

import random

✅ Basic Random Numbers:

print(random.randint(1, 10))      # Random int between 1 and 10
print(random.uniform(1.5, 5.5))   # Random float between 1.5 and 5.5

✅ Random Choice:

colors = ['red', 'blue', 'green']
print(random.choice(colors))  # Randomly picks one color

✅ Shuffle a List:

cards = [1, 2, 3, 4, 5]
random.shuffle(cards)
print(cards)

✅ Sampling Multiple Items:

print(random.sample(colors, 2))  # Picks 2 unique colors

📊 3. The `statistics` Module

Great for quick descriptive stats on numerical data:

import statistics

✅ Common Functions:

data = [2, 4, 4, 4, 5, 5, 7, 9]

print(statistics.mean(data))      # 5.0
print(statistics.median(data))    # 4.5
print(statistics.mode(data))      # 4
print(statistics.stdev(data))     # Standard deviation
print(statistics.variance(data))  # Variance

🛠️ Real-World Examples

🎮 Random Dice Roll:

dice_roll = random.randint(1, 6)
print("You rolled:", dice_roll)

📈 Data Analysis Summary:

sales = [120, 130, 115, 140, 150]
print("Average Sales:", statistics.mean(sales))

🎲 Simulation of 5 Random Points in a Unit Circle:

points = [(random.uniform(-1,1), random.uniform(-1,1)) for _ in range(5)]
print(points)

📌 Summary

math = mathematical constants and advanced functions
random = randomness for games, simulations, and tests
statistics = quick stats for numerical data

🧪 Practice Challenge

Generate 10 random integers between 1 and 100 and compute:

Mean
Median
Standard deviation
1. Create a function that simulates rolling two dice and summing the results.
2. Use math to calculate the area of a circle with random radius between 1 and 10.

Day 39/100: Working with datetime and calendar in Python

Rahul Gupta — Fri, 01 Aug 2025 13:57:04 +0000

Welcome to Day 39 of your Python journey!
Dates and times are everywhere: scheduling tasks, logging events, timestamping data, or building calendars. Python makes this easy with two powerful modules: datetime and calendar.

Today, you’ll learn how to handle dates, times, and calendars like a pro. ⏰

📦 Importing the Modules

import datetime
import calendar

🕰️ 1. Getting the Current Date and Time

from datetime import datetime

now = datetime.now()
print("Current Date & Time:", now)
print("Date:", now.date())
print("Time:", now.time())

📌 Output:

Current Date & Time: 2025-08-01 14:30:22.123456
Date: 2025-08-01
Time: 14:30:22.123456

📆 2. Creating Custom Dates and Times

from datetime import date, time

# Create a date
my_date = date(2025, 12, 25)
print("Custom Date:", my_date)

# Create a time
my_time = time(14, 30, 0)
print("Custom Time:", my_time)

⏳ 3. Date Arithmetic (Timedelta)

You can perform addition and subtraction with timedelta.

from datetime import timedelta

today = date.today()
future = today + timedelta(days=10)
past = today - timedelta(days=30)

print("Today:", today)
print("10 days later:", future)
print("30 days ago:", past)

🗓️ 4. Formatting and Parsing Dates

✅ Formatting with `strftime()`:

print(now.strftime("%Y-%m-%d %H:%M:%S"))
print(now.strftime("%A, %B %d, %Y"))  # Friday, August 01, 2025

✅ Parsing Strings with `strptime()`:

date_str = "2025-12-31"
parsed_date = datetime.strptime(date_str, "%Y-%m-%d")
print(parsed_date)

📜 5. Working with the `calendar` Module

The calendar module is perfect for generating and exploring calendar data.

✅ Printing a Month Calendar:

import calendar

print(calendar.month(2025, 8))

✅ Checking Leap Years:

print(calendar.isleap(2024))  # True
print(calendar.isleap(2025))  # False

✅ Getting a Year’s Calendar:

print(calendar.calendar(2025))

⏱️ 6. Measuring Time (Performance Testing)

Use datetime to measure execution time:

start = datetime.now()

# Simulate task
for _ in range(1000000):
    pass

end = datetime.now()
print("Execution Time:", end - start)

🧠 Quick Reference for `strftime` Codes:

%Y – Year (2025)
%m – Month (01-12)
%d – Day (01-31)
%H – Hour (00-23)
%M – Minute (00-59)
%S – Second (00-59)
%A – Weekday (Monday)
%B – Month Name (August)

🎯 Practice Challenge

Print today’s date and time in the format: Friday, 01 August 2025 - 02:30 PM
Ask the user for their birthdate and calculate their age in days.
Display the calendar for the current month.

🧾 Summary

Use datetime to handle dates, times, and timedeltas.
Use strftime() and strptime() for formatting and parsing.
Use calendar to display calendars and check leap years.

Day 38/100: Advanced input() Handling in Python

Rahul Gupta — Wed, 23 Jul 2025 16:52:49 +0000

By now, you're familiar with Python's input() function. But today, we’ll take it up a notch and learn how to make it smarter, safer, and more user-friendly.

Let’s explore:

Type conversion
Error handling
Custom prompts
Looping until valid input
Handling lists, multiple values, and booleans

🧠 The Basics of `input()`

name = input("Enter your name: ")
print(f"Hello, {name}!")

But here’s the catch — input() always returns a string.
So for numbers and other types, you must convert it manually.

🔁 Converting Input to Numbers

age = int(input("Enter your age: "))
print(f"You will be {age + 1} next year.")

If the user enters something invalid (like "hello"), this throws an error.

🛡️ Safe Input with `try-except`

Let’s make it safer:

try:
    age = int(input("Enter your age: "))
    print(f"You entered: {age}")
except ValueError:
    print("That's not a valid number!")

🔄 Loop Until Valid Input

Use a loop to ask repeatedly until correct input is given:

while True:
    user_input = input("Enter a number: ")
    try:
        number = float(user_input)
        break
    except ValueError:
        print("Please enter a valid number!")

🔢 Getting Multiple Inputs

Split input with .split():

data = input("Enter your name and age (e.g., John 25): ")
name, age = data.split()
print(f"Name: {name}, Age: {age}")

To convert age:

age = int(age)

Or using list comprehension:

nums = input("Enter 3 numbers separated by space: ")
numbers = [int(x) for x in nums.split()]
print(numbers)

✅ Accepting Boolean Input

For yes/no or true/false, use .lower() and compare:

ans = input("Do you agree? (yes/no): ").strip().lower()
if ans in ["yes", "y"]:
    print("Agreed!")
elif ans in ["no", "n"]:
    print("Not agreed.")
else:
    print("Invalid response.")

🛠️ Creating Custom Input Functions

You can wrap all of this in reusable functions:

def get_int(prompt):
    while True:
        try:
            return int(input(prompt))
        except ValueError:
            print("Please enter a valid integer.")

age = get_int("Enter your age: ")
print(age)

📦 Parsing JSON from Input

import json

data = input("Enter JSON: ")
try:
    obj = json.loads(data)
    print("Parsed:", obj)
except json.JSONDecodeError:
    print("Invalid JSON")

🧪 Extra: Input with Defaults

Python doesn't natively support defaults with input(), but you can simulate:

def input_with_default(prompt, default="yes"):
    user_input = input(f"{prompt} [{default}]: ")
    return user_input.strip() or default

response = input_with_default("Continue?")
print(response)

🧾 Summary

Task	Solution Example
Basic input	`input("Enter name: ")`
Convert to number	`int(input(...))`
Safe input (try-except)	Use `try-except` for validation
Loop until valid	`while True` with `break`
Split multiple inputs	`input().split()`
List of values	`[int(x) for x in input().split()]`
Boolean input	Check for `"yes"/"no"` variants
JSON parsing	Use `json.loads()`
Input with default fallback	Simulate with string check

✅ TL;DR

input() always returns strings — convert and validate!
Wrap your logic in reusable functions for cleaner code.
Use loops + try-except for bulletproof input handling.
Make your scripts interactive and user-proof.

Day 37/100: Using eval() Safely in Python

Rahul Gupta — Wed, 23 Jul 2025 16:51:08 +0000

Welcome to Day 37 of your Python journey!

Today we’re discussing a very powerful but potentially dangerous function: eval().

Let’s learn:

What eval() does
Why it can be risky
And most importantly — how to use it safely (or avoid it)

🧠 What is `eval()`?

eval() evaluates a string as a Python expression and returns the result.

Example:

result = eval("3 + 5")
print(result)  # Output: 8

It’s like turning a string into live Python code. Powerful, right? But that’s also what makes it risky. 🧨

🚨 Why `eval()` is Dangerous

If you allow users to input code, eval() can execute malicious commands.

Dangerous Example:

user_input = "__import__('os').system('rm -rf /')"
eval(user_input)  # 😱 Can delete your entire system!

Even something innocent-looking like eval("open('secret.txt').read()") can leak private files.

✅ When to Use `eval()` (if ever)

Use eval() only if:

You fully control the input (no user interaction).
You really need dynamic evaluation.

Still, prefer safer alternatives.

🔐 Safer Alternatives

1. `ast.literal_eval()` (Best Alternative)

Safely evaluates literals (strings, numbers, lists, dicts).

import ast

expr = "{'name': 'Alice', 'age': 30}"
safe_result = ast.literal_eval(expr)
print(safe_result)  # {'name': 'Alice', 'age': 30}

This won't execute functions or import modules. ✅

2. Use JSON

For structured data, prefer JSON.

import json

data = json.loads('{"a": 10, "b": 20}')
print(data)

⚙️ Restricted `eval()` with Globals and Locals

You can limit access by passing restricted dictionaries:

safe_globals = {"__builtins__": None}
safe_locals = {"x": 10, "y": 5}

expr = "x + y"
result = eval(expr, safe_globals, safe_locals)
print(result)  # Output: 15

Still, this should be your last resort — not a go-to solution.

🛠️ Use Cases (When It's Justified)

Tiny calculators or math evaluators (with sandboxing)
Dynamic expression evaluation in controlled internal tools
Data science notebooks with fully trusted inputs

🔄 Summary

Feature	Use When	Safer Alternative
`eval()`	You control the input	`ast.literal_eval()`
User input eval	Never (unless sandboxed)	JSON or `ast` parsing
Complex logic	Avoid or sandbox properly	Use logic in functions

✅ TL;DR

eval() executes Python code from strings — powerful but dangerous.
Never use it on untrusted input.
Prefer ast.literal_eval() or json for safe parsing.
Restrict scope with globals and locals if you must use it.

Day 36/100: Working with JSON in Python

Rahul Gupta — Wed, 23 Jul 2025 16:47:45 +0000

Welcome to Day 36 of your Python learning journey!
Today, we dive into a real-world skill: working with JSON data in Python.
JSON (JavaScript Object Notation) is everywhere—APIs, config files, databases, and web apps use it to store and exchange data.

Let’s learn how to read, write, parse, and manipulate JSON in Python with ease! 🧠

📦 What is JSON?

JSON is a lightweight data format similar to Python dictionaries and lists.

Example JSON:

{
  "name": "Alice",
  "age": 30,
  "skills": ["Python", "Data Science"]
}

📚 Python's `json` Module

Python provides a built-in module named json to handle JSON data.

import json

📥 Converting JSON to Python (Deserialization)

You can load JSON strings into Python dictionaries using json.loads().

import json

json_str = '{"name": "Alice", "age": 30, "skills": ["Python", "Data Science"]}'
data = json.loads(json_str)

print(data["name"])  # Output: Alice
print(type(data))    # <class 'dict'>

📤 Converting Python to JSON (Serialization)

You can convert Python objects (dict, list, etc.) into JSON strings using json.dumps().

person = {
    "name": "Bob",
    "age": 25,
    "skills": ["JavaScript", "React"]
}

json_data = json.dumps(person)
print(json_data)

📝 Pretty Printing JSON

You can format JSON output with indentation for better readability:

print(json.dumps(person, indent=2))

📁 Reading JSON from a File

with open('data.json', 'r') as file:
    data = json.load(file)

print(data["name"])

💾 Writing JSON to a File

with open('output.json', 'w') as file:
    json.dump(person, file, indent=4)

🔁 JSON and Python Data Types

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

🧨 Handling Errors

When working with JSON, handle decoding errors using try-except.

try:
    data = json.loads('{"name": "Alice", "age": }')  # Invalid JSON
except json.JSONDecodeError as e:
    print("Error parsing JSON:", e)

✅ Practical Use Case: Fetching API Data

import requests
import json

response = requests.get("https://jsonplaceholder.typicode.com/users")
users = response.json()

for user in users:
    print(user['name'], '-', user['email'])

🛠️ Summary

Use json.loads() to parse JSON strings.
Use json.dumps() to create JSON strings from Python data.
Use json.load() and json.dump() to work with JSON files.
JSON is used widely in APIs, config files, and more.

Day 35/100: Practice Problem – Data Structure Challenge

Rahul Gupta — Wed, 23 Jul 2025 16:46:26 +0000

Welcome to Day 35 of your Python journey!
After diving deep into lists, dictionaries, sets, comprehensions, and modules like itertools and collections, it’s time to put your knowledge to the test.

Let’s solve a fun and practical data structure challenge! 💡

🎯 Challenge Overview

You’re given a list of orders placed on an online store. Each order contains:

Customer name
Product name
Quantity
Price per unit

Your task is to:

Calculate the total amount spent by each customer.
Find the top 3 products based on total quantity sold.
Show the most valuable single order (by total value).

📦 Sample Data

orders = [
    {'customer': 'Alice', 'product': 'Pen', 'quantity': 3, 'price': 5},
    {'customer': 'Bob', 'product': 'Notebook', 'quantity': 2, 'price': 15},
    {'customer': 'Alice', 'product': 'Notebook', 'quantity': 1, 'price': 15},
    {'customer': 'Dave', 'product': 'Pen', 'quantity': 10, 'price': 5},
    {'customer': 'Carol', 'product': 'Pen', 'quantity': 1, 'price': 5},
    {'customer': 'Bob', 'product': 'Pen', 'quantity': 2, 'price': 5},
    {'customer': 'Alice', 'product': 'Pencil', 'quantity': 5, 'price': 2},
]

🛠️ Step-by-Step Solution

✅ 1. Total Amount Spent by Each Customer

from collections import defaultdict

customer_totals = defaultdict(float)

for order in orders:
    name = order['customer']
    total = order['quantity'] * order['price']
    customer_totals[name] += total

print("💰 Total spent by each customer:")
for customer, total in customer_totals.items():
    print(f"{customer}: ₹{total}")

✅ 2. Top 3 Products by Quantity Sold

from collections import Counter

product_counter = Counter()

for order in orders:
    product_counter[order['product']] += order['quantity']

print("\n📦 Top 3 products sold:")
for product, qty in product_counter.most_common(3):
    print(f"{product}: {qty} units")

✅ 3. Most Valuable Single Order

max_order = max(orders, key=lambda o: o['quantity'] * o['price'])

print("\n💎 Most valuable order:")
print(max_order)

🔎 Output Example

💰 Total spent by each customer:
Alice: ₹40.0
Bob: ₹45.0
Dave: ₹50.0
Carol: ₹5.0

📦 Top 3 products sold:
Pen: 16 units
Pencil: 5 units
Notebook: 3 units

💎 Most valuable order:
{'customer': 'Dave', 'product': 'Pen', 'quantity': 10, 'price': 5}

🧠 Concepts Reviewed

defaultdict for grouped totals
Counter for frequency counting
max() with custom key
Looping and dictionary access
Clean, readable logic

🧪 Try This Extension

Sort the customers by total spending.
Show total revenue per product.
Return top customers who spent more than ₹40.

Day 34/100: Using itertools for Advanced Iteration

Rahul Gupta — Wed, 23 Jul 2025 16:40:47 +0000

Welcome to Day 34 of your Python journey!
Today, we’ll dive into one of Python’s most powerful standard libraries: itertools.

If you’ve ever wanted to:

generate combinations,
create infinite sequences,
or chain multiple iterables,

…then itertools is your best friend.

🧰 What is `itertools`?

The itertools module provides a collection of fast, memory-efficient tools that work on iterators and help you loop like a pro.

🔢 1. Infinite Iterators

These functions produce data endlessly (until you stop them).

`count(start=0, step=1)`

from itertools import count

for i in count(10, 2):
    print(i)
    if i > 20:
        break

Output: 10, 12, 14, 16, 18, 20, 22

`cycle(iterable)`

Loops through an iterable forever.

from itertools import cycle

for item in cycle(['A', 'B', 'C']):
    print(item)
    break  # Use `break` to stop it

`repeat(item, times)`

Repeats the same item a number of times.

from itertools import repeat

for item in repeat('Hello', 3):
    print(item)

Output:
Hello
Hello
Hello

🔁 2. Combinatoric Iterators

Perfect for generating permutations, combinations, and Cartesian products.

`product()`

from itertools import product

colors = ['red', 'blue']
sizes = ['S', 'M']

for combo in product(colors, sizes):
    print(combo)

Output:
('red', 'S'), ('red', 'M'), ('blue', 'S'), ('blue', 'M')

`permutations(iterable, r=None)`

All possible ordered arrangements.

from itertools import permutations

for p in permutations([1, 2, 3], 2):
    print(p)

`combinations(iterable, r)`

All possible unordered combinations.

from itertools import combinations

for c in combinations([1, 2, 3], 2):
    print(c)

🔗 3. Utility Iterators

Useful tools for filtering, chaining, slicing, and grouping iterables.

`chain()`

Joins multiple iterables together.

from itertools import chain

for i in chain([1, 2], ['A', 'B']):
    print(i)

`islice()`

Slices an iterator like a list.

from itertools import islice

for i in islice(range(100), 10, 15):
    print(i)

Output: 10, 11, 12, 13, 14

`compress(data, selectors)`

Filters data by truthy values in selectors.

from itertools import compress

data = 'ABCDEF'
selectors = [1, 0, 1, 0, 1, 0]

print(list(compress(data, selectors)))  # ['A', 'C', 'E']

📌 Real Use Cases

Task	`itertools` Function
Generate all outfit combos	`product()`
Slice big data	`islice()`
Infinite loop through status	`cycle()`
Vote tally simulation	`repeat()`
Schedule permutations	`permutations()`

🧪 Mini Practice

Get all 3-letter permutations of "ABC".
Generate all size-color combinations from two lists.
Chain 3 lists into one iterator.

🧠 Pro Tip

itertools returns iterators, not lists.
Use list() to consume them, but remember—they are memory efficient only when you don’t convert them.

DEV Community: Rahul Gupta

Day 45/100 – Reading/Writing JSON in Python

1. What is JSON?

2. Python’s json Module

3. Reading JSON (Deserialization)

4. Writing JSON (Serialization)

5. Converting JSON Strings

6. Common Pitfalls

7. Mini Example

Notes

Challenge

Day 44/100 – Working with CSV Files in Python

1. What is a CSV File?

2. Reading CSV Files

3. Writing to CSV Files

4. Using DictReader and DictWriter

5. Handling Different Delimiters

6. Best Practices

Challenge for You

Day 43/100: File Modes and Error Handling in Python

📄 1. File Modes Overview

Examples:

🛡️ 2. Error Handling in File Operations

⚡ 3. Combining Modes and Exception Handling

🧠 4. Tips for Working with File Modes

🛠️ Practice Challenge

Day 42/100: Writing Files and Using Context Managers in Python

📥 1. Writing Files with open()

📄 2. Appending to Files

🔁 3. Writing Multiple Lines

🧠 4. Using Context Managers (with)

Why use with?

📌 5. Writing Different Data Types

🔄 6. Handling Errors Safely

🛠️ Real-World Example: Logging System

📜 Summary

🧪 Practice Challenge

Day 41/100: Reading Files with open() in Python

📥 1. The open() Function Basics

📄 2. Reading the Whole File

📜 3. Reading Line by Line

readline() – Reads one line at a time:

readlines() – Returns all lines as a list:

📏 4. Reading Fixed Amount of Characters

🔄 5. Iterating Over a File Object

🧨 6. Handling Errors Safely

🛠️ Real-World Example: Count Words in a File

📌 Summary

🧪 Practice Challenge

Day 40/100: Python math, random, and statistics Modules

🧮 1. The math Module

✅ Common Functions:

✅ Constants:

✅ Trigonometry:

🎲 2. The random Module

✅ Basic Random Numbers:

✅ Random Choice:

✅ Shuffle a List:

✅ Sampling Multiple Items:

📊 3. The statistics Module

✅ Common Functions:

🛠️ Real-World Examples

🎮 Random Dice Roll:

📈 Data Analysis Summary:

🎲 Simulation of 5 Random Points in a Unit Circle:

📌 Summary

🧪 Practice Challenge

Day 39/100: Working with datetime and calendar in Python

📦 Importing the Modules

🕰️ 1. Getting the Current Date and Time

📌 Output:

📆 2. Creating Custom Dates and Times

⏳ 3. Date Arithmetic (Timedelta)

🗓️ 4. Formatting and Parsing Dates

✅ Formatting with strftime():

✅ Parsing Strings with strptime():

📜 5. Working with the calendar Module

✅ Printing a Month Calendar:

✅ Checking Leap Years:

✅ Getting a Year’s Calendar:

2. Python’s `json` Module

📥 1. Writing Files with `open()`

🧠 4. Using Context Managers (`with`)

Why use `with`?

📥 1. The `open()` Function Basics

`readline()` – Reads one line at a time:

`readlines()` – Returns all lines as a list:

🧮 1. The `math` Module

🎲 2. The `random` Module

📊 3. The `statistics` Module

✅ Formatting with `strftime()`:

✅ Parsing Strings with `strptime()`:

📜 5. Working with the `calendar` Module

🧠 Quick Reference for `strftime` Codes:

🧠 The Basics of `input()`

🛡️ Safe Input with `try-except`

🧠 What is `eval()`?

🚨 Why `eval()` is Dangerous

✅ When to Use `eval()` (if ever)

1. `ast.literal_eval()` (Best Alternative)

⚙️ Restricted `eval()` with Globals and Locals

📚 Python's `json` Module

🧰 What is `itertools`?

`count(start=0, step=1)`

`cycle(iterable)`

`repeat(item, times)`

`product()`

`permutations(iterable, r=None)`

`combinations(iterable, r)`

`chain()`

`islice()`

`compress(data, selectors)`