Why Python? A Complete Beginner’s Guide to Mastering the Language

1. What is Python?

Python is a high‑level, object‑oriented programming language. Most beginners love Python because its simplicity and versatility make it one of the first languages to learn. It enjoys strong community support and continues to grow.

This beginner‑focused Python tutorial covers the fundamentals of the language and shows how to get started, including downloading, installing, and using popular IDEs, as well as detailed discussion of Jupyter features.

2. What are Python's uses?

Python is used in many applications, platforms, and services. Below are some key areas.

2.1 Web Development

Pre‑built Python libraries simplify web development. Its clear syntax reduces coding time, enabling rapid prototyping and faster ROI. Built‑in testing frameworks help deliver error‑free code, and well‑supported frameworks accelerate implementation without sacrificing performance.

2.2 Internet of Things

IoT connects embedded systems to the internet, playing a crucial role in big data, machine learning, data analysis, wireless networks, and cyber‑physical systems, often requiring real‑time analytics.

Python’s scalability, portability, and embeddability make it ideal for IoT projects, allowing it to run on multiple single‑board computers regardless of OS or architecture.

Python is also excellent for managing and organizing complex data, which is especially useful for large‑scale IoT systems, and its close ties to scientific computing further enhance its suitability.

2.3 Machine Learning

Python leads in machine learning and data science because of extensive open‑source library support, efficient syntax, easy integration with other languages, low entry barrier, and cross‑platform scalability.

Extensive open‑source library support

Efficient and precise syntax

Easy integration with other programming languages

Low learning curve

Scalable across operating systems and architectures

3. How to Install Python?

If you are a Windows user and have installed Python via the Anaconda distribution from Anaconda.org, go to "Download Anaconda" and download the latest version for Python 3.6.

After downloading, the installation process is straightforward. Then launch an IDE to start coding.

Once Python is installed, you will have multiple IDEs or text editors available.

For text editors you can use Sublime, Notepad++, etc. For IDEs you can use Jupyter, Wingware, Komodo, PyCharm, or Spyder.

Python offers many packages such as numpy, pandas, seaborn for visualization, scipy for computation and statistics, xlrd, openpyxl, matplotlib, and io.

(See accompanying video at the end)

4. Why Choose Python?

Python has become the preferred language for data science and machine learning applications. Compared with other languages, it is fast, and even R is comparable.

Python is a rapid compiler. Being Java‑based, you can extend applications beyond analytics, modeling, and statistical modeling. You can create web applications and integrate them directly with backend analytical models.

Python integrates easily with other platforms and languages. Its universal object‑oriented architecture makes transition for IT developers, analysts, and programmers smooth, and it enjoys excellent documentation support.

7 Reasons to Use Python

Readable and maintainable code

Multiple programming paradigms

Compatibility with major platforms and systems

Powerful standard library

Open‑source frameworks and tools

Simplified software development

Test‑driven development

5. R vs Python?

R is designed for statistical analysis; Python is a general‑purpose language. Both are essential for handling large datasets, solving machine‑learning problems, and creating complex visualizations.

Python was originally created to automate routine tasks, but it has evolved into a powerful tool for data analysis.

R is an open‑source language and software for graphics and data analysis, runnable on any computer system and widely used by data miners and statisticians.

Choosing between R and Python depends on the specific needs of data scientists; Python’s generality and consistent syntax often give it an edge.

Python Programming Language

Python was inspired by Modula‑3, ABC, and C.

Python focuses on code readability and productivity.

Its simple syntax makes development and debugging easier.

Indentation affects meaning.

All functions are usually written in the same style.

Python is flexible and can be used for web scripting.

It has a relatively low learning curve due to its emphasis on simplicity and readability.

It is suitable for beginners.

Its package index is called PyPI, a repository of libraries. Users can contribute, though it can be challenging in practice.

RPy2 allows running R code from Python.

In 2014, Dice Tech reported an average salary of $94,139 for experienced experts.

It is mainly used when integrating analytical data with web applications or when statistical information is needed for database production.

Although data‑processing capabilities have improved, handling large data remains challenging because many packages are still in early stages.

You must use tools like pandas and NumPy for data analysis.

Available IDEs include Spyder and IPython Notebook.

R Programming

S programming language inspired R.

Emphasizes user‑friendly data analysis, graphical models, and statistics.

Statistical models require only a few lines of code, making them a bit difficult to use.

R style sheets exist but are rarely used.

Many ways to represent or write the same functionality block.

Facilitates complex R formulas for many statistical models and tests.

The learning curve is steep at first but becomes easy for advanced topics.

Not difficult for professional programmers.

CRAN is the comprehensive R archive network, an easy‑to‑use repository.

The rpython package allows running Python code from R.

In 2014, Dice Tech reported an average salary of $115,531 for experienced experts.

Primarily used for independent analysis or server‑side analytics.

Key tasks for beginners are easier; few lines of code can write statistical methods.

Very suitable for handling large data packages and testing formulas.

R does not require additional packages for basic analysis; packages like dplyr suffice for large datasets.

Uses the RStudio IDE.

7. What are the most popular Python IDEs?

Spyder

Wing

Tony

Atom

Jupyter

Komodo

Wing

8. Which IDE is best for Python?

Jupyter is the best and most widely used IDE for Python. Below we show how to set up Jupyter Notebook and explore its features.

9. How to Power Jupyter Notebook

Follow these steps to start a Jupyter notebook:

Open the Anaconda Prompt (available if you installed via the Anaconda installer).

The prompt shows the default path assigned to your user.

Navigate to the folder where you want to open the notebook (e.g., cd Desktop && cd Python).

Run the command jupyter notebook to launch Jupyter.

Press Enter; the notebook opens in your local host.

The Anaconda prompt now shows the notebook homepage path.

Open a new Python notebook, rename it as desired, and start coding.

Keep the Anaconda prompt active while using Jupyter; closing it stops the kernel.

10. Features in Python Notebook (Jupyter)

The toolbar includes File, Edit, View, Insert, Cell, Kernel, Widget, and Help options. Below are some key features.

File Options

Save and Checkpoint – Automatic checkpoints allow you to revert to a stable state if an error occurs.

Download as – You can download notebooks in various formats, such as the classic .ipynb extension or as a .py script.

Close and Halt – This command shuts down all kernels at the current time and pauses processes.

Edit Options

Includes cut cell, copy cell, paste, delete, split cell, move up, move down, etc.

A cell is a box where you type code; running the cell executes the code and shows output.

Run a cell with the Run button or the shortcut Shift + Enter .

You can view keyboard shortcuts under the Help menu.

View Options

You can toggle the header, toolbar, and line numbers.

Insert Options

Insert a cell above or below the current cell.

Cell Options

Run All runs all cells in the notebook. Run All Above runs all cells above the selected cell. Run All Below runs all cells below the selected cell.

Cell types include Code, Markdown, and Raw. Markdown cells render formatted text and do not execute code.

Help Options

Shows available libraries and packages, and provides guides or reference books for selected packages.

11. Comments

Comments describe program logic and module purpose, making code understandable for others.

During testing, comments can disable code sections.

Python comments start with #. They can be on their own line or after code on the same line.

# comment in separate line
x = x * 5

x = x * 5  # multiply by 5; comment in same line

# this function is going to divide two variables
# the second value should not be zero
# if it is zero, error is thrown
def divide(first, second):
    pass

Multi‑line comments are created by prefixing each line with #. If # appears inside quotes, it is part of the string.

str = "# hello world"

12. Variables

Variables are containers that store data. Assigning a value creates a variable; Python does not require explicit declaration.

numOfBoxes = 7
ownerName = "Karthik"
print("numOfBoxes= ", numOfBoxes)
print("ownerName= ", ownerName)

Variable naming rules:

Start with a letter or underscore.

Subsequent characters can be alphanumeric or underscore.

Names are case‑sensitive.

# valid names
numOfBoxes = 7
_num_of_boxes = 10  # different variable
_NUM_OF_BOXES = 15  # different due to case
ownerName = "Mango"
ownerName2 = "Charan"

# invalid names
2ownerName = "David"  # cannot start with a number
owner-name = "Ram"   # hyphen not allowed
owner name = "Krish"  # space not allowed

Multiple assignment:

# assign different values
length, width, depth = 5, 8, 7
print(length)
print(width)
print(depth)

# assign same value to many variables
length = width = depth = 5
print(length)
print(width)
print(depth)

13. Operators

Operators manipulate variables and values. Python supports the following categories:

Arithmetic Operators

Assignment Operators

Comparison Operators

Logical Operators

Identity Operators

Membership Operators

Bitwise Operators

Arithmetic Operators

Perform basic math operations.

a = 10
b = 6
print(a + b)   # addition
print(a - b)   # subtraction
print(a * b)   # multiplication
print(a / b)   # division
print(a % b)   # modulus
print(a ** b)  # exponentiation
print(a // b)  # floor division

Modulus returns the remainder; floor division returns the integer part of the quotient.

Assignment Operators

Assign values to variables, optionally using expressions.

a = 7  # assign value to a
b = a  # assign value of a to b
c = a + b - 2  # calculate expression and assign to c

b += 2   # equivalent to b = b + 2
b -= 2   # equivalent to b = b - 2
b *= 2   # equivalent to b = b * 2
b /= 2   # equivalent to b = b / 2
b %= 2   # equivalent to b = b % 2
b //= 2  # equivalent to b = b // 2
b **= 2  # equivalent to b = b ** 2
b &= 2   # equivalent to b = b & 2
b |= 2   # equivalent to b = b | 2
b ^= 2   # equivalent to b = b ^ 2
b >>= 2  # equivalent to b = b >> 2
b <<= 2  # equivalent to b = b << 2

Comparison Operators

Compare two values and return a Boolean.

a = 3
b = 7
print(a == b)   # True if equal
print(a != b)   # True if not equal
print(a > b)    # True if a greater than b
print(a < b)    # True if a less than b
print(a >= b)   # True if a greater or equal
print(a <= b)   # True if a less or equal

Example in an if statement:

a = 3
b = 7
if a < b:
    print("first number is less than second one")
else:
    print("first number is greater or equal to second one")

Logical Operators

Combine multiple Boolean expressions.

a = 5
b = 8
print(a > 3 and a < 7)   # True if both conditions true
print(a > 6 or b < 7)    # True if one condition true
print(not(a > 3))        # Inverse of condition

Identity Operators

Check whether two objects refer to the same location.

a = ["hello", "world"]
b = ["hello", "world"]
c = a
print(a is c)      # True, same object
print(a is b)      # False, different objects
print(a == b)      # True, values equal
print(a is not c)   # False
print(a is not b)   # True

Membership Operators

Test for presence of an element in a list.

a = ["hello", "world"]
print("world" in a)   # True
print("world" not in a) # False

Bitwise Operators

Operate on binary representations of integers.

a = 1   # binary 01
b = 2   # binary 10
print(a & b)   # AND -> 0
print(a | b)   # OR  -> 3
print(a ^ b)   # XOR -> 3
print(~b)      # NOT -> -3 (two's complement)
print(b << 2)  # left shift -> 8
print(b >> 1)  # right shift -> 1

21. Lambda

Lambda creates anonymous small functions with a single expression.

# lambda that adds 2
growth = lambda givenLength: givenLength + 2
print(growth(25))  # 27

# lambda with two parameters
area = lambda length, breadth: length * breadth
print(area(4, 7))  # 28

Useful for creating function factories:

def growth(n):
    return lambda a: a + n

stretchTwo = growth(2)
stretchThree = growth(3)
print(stretchTwo(7))   # 9
print(stretchThree(7)) # 10

22. Arrays (Lists)

Lists store ordered collections of items.

fruits = ["mango", "apple", "grapes"]
print(fruits)
firstFruit = fruits[0]
print(firstFruit)
fruits[0] = "melon"
print(fruits)
print(len(fruits))  # 3
for fruit in fruits:
    print(fruit)
fruits.append("guava")
print(fruits)
fruits.pop(1)
print(fruits)
fruits.remove("grapes")
print(fruits)

23. Classes

Classes define blueprints for objects.

# simple class
class Fruit:
    name = "mango"

oneFruit = Fruit()
print(oneFruit.name)

# class with __init__
class Fruit:
    def __init__(self, name, color):
        self.name = name
        self.color = color

oneFruit = Fruit("mango", "yellow")
print(oneFruit.name)
print(oneFruit.color)

# method example
class Fruit:
    def __init__(self, name, color):
        self.name = name
        self.color = color
    def makeJuice(self):
        print("Made " + self.name + " juice. It will be in " + self.color + " color.")

oneFruit = Fruit("mango", "yellow")
oneFruit.makeJuice()
oneFruit.color = "red"

24. Inheritance

Inheritance allows a subclass to extend a parent class.

class Vehicle:
    def __init__(self, make, color):
        self.make = make
        self.color = color
    def display(self):
        print("make= " + self.make + " color= " + self.color)

class Car(Vehicle):
    def __init__(self, make, color, numOfSeats):
        super().__init__(make, color)
        self.numOfSeats = numOfSeats
    def display(self):
        super().display()
        print("number of seats= " + str(self.numOfSeats))
    def wipeWindshield(self):
        print("turned on wiper")

newCar = Car("2019", "orange", 5)
newCar.display()
newCar.wipeWindshield()

25. Iterators

Iterators provide a way to traverse collections.

# using built‑in iterators
fruitTuple = ("mango", "apple", "grapes")
fruitIter = iter(fruitTuple)
print(next(fruitIter))
print(next(fruitIter))
print(next(fruitIter))

# custom iterator – Fibonacci
class Fibonacci:
    def __iter__(self):
        self.first = 1
        self.second = 2
        return self
    def __next__(self):
        curr = self.first + self.second
        self.first = self.second
        self.second = curr
        return curr

fibo = Fibonacci()
for _ in range(5):
    print(next(fibo))

26. Scope

def func1():
    a = 5
    print(a)
func1()
# print(a)  # would raise NameError

# global scope
a = 5
def func1():
    print(a)
func1()
print(a)

# local vs global
a = 5
def func1():
    a = 7
    print(a)
func1()
print(a)

# using global keyword
def func1():
    global a
    a = 10
    print(a)
func1()
print(a)

27. Modules

Modules are reusable files containing functions and variables.

# inventory.py
fruits = ["mango", "apple", "grapes"]
def countStock():
    return len(fruits)

# main script
import inventory
print(inventory.countStock())
print(inventory.fruits)

import inventory as inv
print(inv.countStock())
print(inv.fruits)

from inventory import countStock
print(countStock())
# print(fruits)  # NameError because fruits not imported

28. Date

import datetime
today = datetime.datetime.now()
print(today)

pastDate = datetime.datetime(2020, 2, 29)
print(pastDate)
print(pastDate.strftime("%B"))  # Full month name

29. JSON Format

import json
jsonStr = '{ "name" : "mango", "price" : 100, "color" : "yellow" }'
jsonData = json.loads(jsonStr)
print(jsonData["color"])
print(jsonData)

fruitsDictionary = {"name":"mango", "price":100, "color":"yellow"}
jsonStr = json.dumps(fruitsDictionary)
print(jsonStr)
jsonStr = json.dumps(fruitsDictionary, indent=5)
print(jsonStr)
jsonStr = json.dumps(fruitsDictionary, indent=5, sort_keys=True)
print(jsonStr)

30. Regular Expressions

import re
givenStr = "Hello world, good morning"
outRegex = re.search("^Hello", givenStr)
print(outRegex)

31. Picture‑in‑Picture (Package Management)

PIP installs Python packages.

pip install <package_name>
pip uninstall <package_name>
pip list   # shows installed packages

32. Try, Except

try:
    print(a)  # a is undefined
except:
    print("error occurred, please check if a is defined")
finally:
    print("We are here after try and except")

# raising custom exception
a = 0
if a == 0:
    raise Exception("cannot divide by zero")

33. User Input

city = input("Where do you live? ")
print("city you live is : " + city)

34. String Formatting

tStr = "The fruit {} is {} color."
print(tStr.format("mango", "yellow"))
print(tStr.format("apple", "red"))

# using indices
tStr = "The fruit {0} is {1} color. {0} is good for health"
print(tStr.format("mango", "yellow"))
print(tStr.format("apple", "red"))

# using named placeholders
tStr = "The fruit {name} is {color} color. {name} is good for health"
print(tStr.format(name="mango", color="yellow"))
print(tStr.format(name="apple", color="red"))

35. Python Data Types

Python supports five standard data types:

Numbers (int, long, float, complex)

Strings

Lists

Tuples

Dictionaries

Numbers

a = 4
b = 6

Four numeric types: int, long, float, complex.

Strings

str1 = 'hello javatpoint'
str2 = 'how are you'
print(str1[0:2])
print(str1[4])
print(str1 * 2)
print(str1 + str2)

Lists

l = [1, "hi", "python", 2]
print(l[3:])
print(l[0:2])
print(l)
print(l + l)
print(l * 3)

Tuples

t = ("hi", "python", 2)
print(t[1:])
print(t[0:1])
print(t)
print(t + t)
print(t * 3)
print(type(t))
# t[2] = "hi"  # raises TypeError

Dictionaries

d = {1:"Jimmy", 2:"Alex", 3:"John", 4:"Mike"}
print("First name is " + d[1])
print("Second name is " + d[4])
print(d)
print(d.keys())
print(d.values())

36. Control Flow Statements

If‑Else Statements

a = 10
b = 20
if b > a:
    print("b is greater than a")

if a > b:
    print("a is greater than b")
else:
    print("b is greater than a")

Elif Example

a = 10
b = 20
c = 30
if a > b and a > c:
    print("a is the largest")
elif b > a and b > c:
    print("b is the largest")
else:
    print("c is the largest")

While Loop

i = 1
while i <= 5:
    print(i)
    i += 1

# multiplication table for 2
i = 1
n = 2
while i <= 10:
    print(f"{n} * {i} = {n * i}")
    i += 1

For Loop

colors = ["blue", "green", "red"]
for i in colors:
    print(i)

# nested loops
colors = ["green", "yellow", "pink"]
items = ["chair", "book", "phone"]
for i in colors:
    for j in items:
        print(i, j)

37. File Handling

# reading a file
f = open("fruits.txt")
print(f.read())

# reading line by line
f = open("fruits.txt")
print(f.readline())
print(f.readline())

# using for loop
f = open("fruits.txt")
for line in f:
    print(line)
f.close()

# appending to a file
f = open("fruits.txt", "a")
f.write("guava is green")
f.close()

# overwriting a file
f = open("fruits.txt", "w")
f.write("only this line")
f.close()

# creating a new file (fails if exists)
# f = open("fruits.txt", "x")

# deleting a file
import os
os.remove("fruits.txt")

# checking existence before deletion
filename = "fruits.txt"
if os.path.exists(filename):
    os.remove(filename)
else:
    print(filename + " does not exist")

# removing a directory
os.rmdir("unwantedFolder")

38. Creating Functions in Python

def add_10(x):
    return 10 + x
print(add_10(5))  # 15

Functions can include control flow, as shown in the accompanying video (QR code below).

39. Learning Simple Commands Using Python as a Calculator

# comments examples
# This is a comment
counter = 100  # comment after code
#   # comment after spaces
text = "# this is not a comment because # is inside quotes"

# basic arithmetic
print(2 + 2)          # 4
print(50 - 5 * 4)      # 30
print((50 - 5 * 4) / 6)  # 5.0
print(8 / 5)          # 1.6
print(16 / 3)          # 5.333...
print(16 // 3)         # 5
print(16 % 3)          # 1
print(5 ** 2)          # 25
print(3 ** 4)          # 81

# variable assignment
height = 20
width = 9 * 5

# using the last printed value (_) in interactive mode
tax = 12.5 / 100
price = 110.50
print(price * tax)   # 13.8125 (assigned to _)
print(price + _)      # 124.3125 (assigned to _)
print(round(_, 2))   # 124.31

Watch the accompanying video by scanning the QR code below to get the full tutorial.

Reply "python video" to receive the video.

— Follow "Python Headlines" for daily resources —

👍 Like to encourage us