Why Python is the Language to Learn Right Now

Every few years, a programming language rises to the top of developer surveys and job listings in a way that is hard to ignore. Python has been doing this consistently since 2015, and in 2024 it sits at number one on the TIOBE index, the Stack Overflow developer survey, and nearly every "most in-demand languages" ranking published by hiring platforms.

Here is what actually drives that popularity:

It reads like English

Python was designed with one philosophy at its core: code should be easy to read. Guido van Rossum, who created Python in 1991, believed that code is read far more often than it is written. The result is a language where you spend less time fighting syntax and more time solving actual problems.

# Calculate GST for a list of prices - this almost reads like a sentence
prices = [299, 499, 1299, 2499]
with_gst = [round(price * 1.18, 2) for price in prices]
print(with_gst)
# [352.82, 588.82, 1532.82, 2948.82]
Python

One language, every domain

Most languages have a primary domain. Java is enterprise backend. JavaScript is the web. C++ is systems programming. Python refuses to be boxed in. The same language powers Zomato's recommendation engine, ISRO's satellite telemetry processing, a data analyst's quarterly sales report, and the fine-tuning of large language models. You learn it once and it opens multiple doors.

The ecosystem is unmatched

Python has over 400,000 packages on PyPI. Libraries like NumPy, Pandas, Scikit-learn, TensorFlow, FastAPI, and Django represent hundreds of thousands of engineering hours distilled into tools you can import in one line. Whatever problem you are trying to solve, someone has almost certainly built a Python library for it.

The job market is real

As of 2024, Python is required or preferred in the majority of data science, machine learning, backend engineering, and automation job postings in India and globally. Companies like Swiggy, Meesho, Razorpay, PhonePe, and nearly every product startup in Bangalore list Python as a core skill. The demand is not abstract - it is in active job listings right now.

Python vs Other Popular Languages

Understanding where Python fits relative to other languages helps you make an informed decision about what to learn and why. This is not about declaring one language "better" - every language was built to solve specific problems. It is about understanding the trade-offs.

The honest trade-off: Speed vs Productivity

Python is interpreted, dynamically typed, and runs slower than compiled languages like C++ or Java. For building a video game engine or an operating system kernel, Python is the wrong choice. But for data analysis, web APIs, machine learning pipelines, and automation scripts - which is where most software jobs actually are - Python's development speed far outweighs its execution speed disadvantage. And when raw performance is needed, Python code calls into C libraries like NumPy under the hood anyway.

Setting Up Python and Jupyter Notebook

Getting Python running on your machine takes about ten minutes. There are two things to install: Python itself, and Jupyter Notebook - the interactive environment we will use for all exercises.

Step 1: Install Python

1. Download Python

   Go to python.org/downloads and download the latest stable version (3.12 or newer). On Windows, make sure to check "Add Python to PATH" during installation. On macOS and Linux, Python may already be installed - run python3 - version in your terminal to check.

2. Verify the installation

   Open a terminal (Command Prompt on Windows, Terminal on Mac/Linux) and run:

python - version
# Expected: Python 3.12.x (or newer)

python -c "print('Python is working!')"
# Expected: Python is working!
Shell

Step 2: Install Jupyter via pip

pip is Python's package manager. It is installed automatically with Python. Use it to install Jupyter:

pip install notebook
# This installs Jupyter Notebook and all its dependencies
Shell

Step 3: Launch Jupyter Notebook

jupyter notebook
# Opens a browser tab at http://localhost:8888
# Navigate to a folder and click "New > Python 3" to create your first notebook
Shell

Why Jupyter Notebook?

Jupyter lets you write and run Python code in small, isolated cells. You see the output immediately below each cell. This makes it ideal for learning because you can experiment, make mistakes, and observe results without running an entire file each time. It is also the standard tool used by data analysts and data scientists worldwide.

Your First Cell

Once Jupyter is open, create a new notebook and type this in the first cell, then press Shift + Enter to run it:

# Your first Python program
name = "Aarav"
city = "Bangalore"
print(f"Hello! My name is {name} and I am from {city}.")

# Output: Hello! My name is Aarav and I am from Bangalore.
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If you see the output, your setup is complete.

Python Syntax and the Rules You Need to Know

Every language has grammar rules. Python's rules are minimal but strict in certain areas. Understanding them from day one saves you hours of debugging confusing errors later.

Indentation in Practice

This is the most important rule to internalize early. Here is what correct and incorrect indentation looks like:

# CORRECT - consistent 4-space indentation
score = 85

if score >= 60:
 print("Passed") # 4 spaces in
 if score >= 85:
 print("Distinction") # 8 spaces in (nested block)
else:
 print("Failed") # 4 spaces in

# WRONG - mixing indentation levels causes IndentationError
if score >= 60:
 print("Passed") # 2 spaces - inconsistent!
 print("Continuing...") # 6 spaces - IndentationError
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Variables and Assignment

# Assigning values
student_name = "Priya Sharma"
age = 21
gpa = 8.7
is_enrolled = True

# Multiple assignment in one line
x, y, z = 10, 20, 30

# Reassigning changes the type - this is valid in Python
value = 42 # integer
value = "hello" # now a string - Python allows this

# Check the type of any variable
print(type(age)) # <class 'int'>
print(type(gpa)) # <class 'float'>
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Printing Output

# Basic print
print("Namaste, World!")

# Print multiple values
print("Name:", student_name, "| Age:", age)

# f-string formatting - the modern, preferred way
print(f"{student_name} is {age} years old with a GPA of {gpa}.")

# Format numbers inside f-strings
price = 1234567.89
print(f"Price: Rs.{price:,.2f}") # Rs.1,234,567.89
Python

Basic Data Types in Python

A data type tells Python what kind of value a variable holds and what operations are allowed on it. Python has a handful of built-in types that cover the vast majority of what you will work with in the beginning. Here is an overview before we go into each one:

Integers and Floats

# Integers - no limit on size in Python
population = 1_428_627_663 # underscores allowed for readability
odi_centuries = 50

# Floats
gst_rate = 0.18
ipl_fee = 1_294_000_000.00

# Arithmetic operators
print(10 + 3) # 13 addition
print(10 - 3) # 7 subtraction
print(10 * 3) # 30 multiplication
print(10 / 3) # 3.333... always returns float
print(10 // 3) # 3 floor division (integer result)
print(10 % 3) # 1 modulo (remainder)
print(10 ** 3) # 1000 exponentiation
Python

Strings

# Creating strings
name = "Rohan Mehta"
city = 'Hyderabad' # single or double quotes, both work
address = """
123, MG Road,
Bangalore - 560001
""" # triple quotes for multiline strings

# Common string operations
print(len(name)) # 11 - length of string
print(name.upper()) # ROHAN MEHTA
print(name.lower()) # rohan mehta
print(name.replace("Mehta", "Sharma")) # Rohan Sharma
print(name.split(" ")) # ['Rohan', 'Mehta']

# Indexing and slicing
print(name[0]) # R - first character
print(name[-1]) # a - last character
print(name[0:5]) # Rohan - slice
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Booleans and Comparison

# Boolean values
is_registered = True
has_paid = False

# Comparison operators return booleans
print(10 > 5) # True
print(10 == 10) # True - note: == not =
print(10 != 5) # True
print(10 >= 10) # True
print(10 < 5) # False

# Logical operators
print(True and False) # False
print(True or False) # True
print(not True) # False

# Real use case
age = 22
has_degree = True

eligible = age >= 21 and has_degree
print(eligible) # True
Python

Type Conversion

# Convert between types explicitly
age_str = "25"
age_int = int(age_str) # string to int

price_int = 299
price_float = float(price_int) # int to float: 299.0

score_float = 87.9
score_int = int(score_float) # float to int: 87 (truncates, does NOT round)

number = 42
number_str = str(number) # int to string: "42"

# Common pattern: reading user input (always a string, must be converted)
age = int(input("Enter your age: "))
Python

Where to Go From Here

You now have Python installed, an understanding of its syntax, a working knowledge of its basic data types, and a clear map of where this journey leads. That is more than most beginners have before writing their first line of code.

The next lecture covers control flow - if/elif/else conditions and how to make your programs make decisions. After that, loops. Then collections. Each concept builds on the last. The roadmap above shows exactly where each piece fits.

Every practice sheet, masterclass, and project on Codeverra is designed to fit into one of the four phases on the roadmap. When you complete a resource, come back to this page and see which box it fills. That progress tracking will keep you oriented as the curriculum grows.