Python Guide: Beginner Basics to Work & Side Gigs

1. Introduction

1.1 What is Python? Why is it popular?

1.1.1 Simple syntax makes it easy to learn

print("Hello, World!")

#include <stdio.h>

int main() {
    printf("Hello, World!n");
    return 0;
}

1.1.2 Versatile usage

• Web development (frameworks like Django, Flask, etc.)
• Data analysis & AI development (pandas, NumPy, TensorFlow, PyTorch, etc.)
• Automation & scraping (selenium, beautifulsoup, etc.)
• Game development (Pygame, etc.)
• Network & security (Scapy, etc.)

1.1.3 Rich libraries and frameworks

1.2 Python is easy for beginners to learn

1.2.1 Readable code structure

x = 10

if x > 5:
    print("x is greater than 5")

1.2.2 Abundant learning resources

1.2.3 Hands‑on learning environment

1.3 Summary

2. Five Reasons to Learn Python

2.1 Simple Syntax Friendly to Beginners

Code Comparison Between Python and Other Languages (Hello, World!)

print("Hello, World!")

public class Main {
    public static void main(String[] args) {
        System.out.println("Hello, World!");
    }
}

2.2 Can Serve a Wide Range of Uses

2.3 Rich Libraries and Frameworks

Example Using a Library (Data Analysis with pandas)

import pandas as pd

# Create sample data
data = {'Name': ['Sato', 'Tanaka', 'Suzuki'],
        'Age': [25, 30, 35],
        'Occupation': ['Engineer', 'Data Scientist', 'Marketer']}

df = pd.DataFrame(data)

# Display data
print(df)

2.4 High Demand and Career Benefits

Average Salary of Python Engineers (Japan)

• Python Engineer (Web Development): ¥5M–¥7M
• Data Scientist (Python usage): ¥6M–¥9M
• AI Engineer (Python usage): ¥7M–¥12M

2.5 Active Community and Easy Learning

Resources for Learning

• Python Official Documentation (free)
• Online Learning Platforms (Progate, Udemy, Coursera, etc.)
• Books (e.g., “Python Introduction” or “Data Analysis with Python”, etc.)
• YouTube Tutorials (free to watch)

2.6 Summary

1. Simple syntax friendly to beginners
2. Can serve a wide range of uses
3. Rich libraries and frameworks
4. High demand and career benefits
5. Active community and easy learning

3. Basic Python Syntax (Beginner-friendly)

3.1 Variables and Data Types

Basics of Variables

name = "Sato"
age = 25
height = 170.5

print(name)  # Sato
print(age)   # 25
print(height) # 170.5

Main Data Types

Type Conversion

age = "25"  # string
age = int(age)  # convert to number
print(age + 5)  # 30

3.2 Conditional Branching (if statement)

Basics of if statements

age = 18

if age >= 20:
    print("Adult")
else:
    print("Minor")

elif (multiple conditions)

score = 85

if score >= 90:
    print("Grade A")
elif score >= 70:
    print("Grade B")
else:
    print("Grade C")

3.3 Loop Processing (for and while statements)

for loop (list iteration)

fruits = ["apple", "banana", "grape"]

for fruit in fruits:
    print(fruit)

apple
banana
grape

for loop using range()

for i in range(5):
    print(i)  # 0, 1, 2, 3, 4

while loop (loop while condition is met)

count = 0

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

3.4 Defining and Using Functions

Basics of Functions

def greet(name):
    print(f"Hello, {name}!")

greet("Sato")

Hello, Sato!

Functions that Return Values

def add(a, b):
    return a + b

result = add(3, 5)
print(result)  # 8

Default Arguments

def greet(name="Guest"):
    print(f"Hello, {name}!")

greet()  # Hello, Guest!
greet("Tanaka")  # Hello, Tanaka!

3.5 Classes and Object-Oriented Programming (Basics)

Basics of Classes

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def introduce(self):
        print(f"I am {self.name}. I am {self.age} years old.")

# Create an instance
person1 = Person("Sato", 25)
person1.introduce()

I am Sato. I am 25 years old.

3.6 Summary

1. Variables and Data Types – Intuitive syntax with no need for type declarations
2. Conditional Branching (if statements) – Simple syntax that’s easy to read
3. Loop Processing (for / while) – Flexible repetitive processing
4. Defining and Using Functions – Improves code reusability
5. Object-Oriented Programming – Leverage classes and objects

4. Setting Up a Python Development Environment

4.1 Installing Python (Windows & Mac)

Installation steps on Windows

1. Visit the Python official website (https://www.python.org/)
2. Download the latest Stable Releases
3. Run the installer and check the following two options

• Add Python to PATH
• Install Now

1. After installation, open Command Prompt (cmd) and run the following command; if the version is displayed, the installation succeeded

python --version

python -V

Installation steps on Mac

1. Install Python using Homebrew

   brew install python

2. After installation, verify the version with the following command

   python3 --version

4.2 Creating and Managing Virtual Environments (venv)

Creating a virtual environment

python -m venv myenv

Activating the virtual environment

• Windows

  myenvScriptsctivate

• Mac/Linux

  source myenv/bin/activate

Deactivating the virtual environment

deactivate

4.3 Managing Python Packages (pip)

Installing libraries

pip install pandas

Listing installed libraries

pip list

Uninstalling a library

pip uninstall pandas

Bulk installation using requirements.txt

1. Create a requirements.txt file and list the required libraries (e.g.):

   numpy
   pandas
   flask

2. Install them all with the following command:

   pip install -r requirements.txt

4.4 Recommended IDEs and Code Editors for Python Development

Recommended IDEs and Editors for Python

How to Install VS Code

1. Download from the VS Code official site (https://code.visualstudio.com/)
2. Install the Python extension
3. Press Ctrl + Shift + P and select “Python: Select Interpreter” to configure the Python environment
4. Write code and run it (e.g., python script.py in the terminal)

4.5 Using Jupyter Notebook (for Data Analysis & AI)

Installing Jupyter Notebook

pip install jupyter

Launching Jupyter Notebook

jupyter notebook

4.6 Summary

1. Installing Python (Windows & Mac)
2. Creating and managing virtual environments (venv)
3. Package management (pip)
4. Recommended IDEs & editors (VS Code, PyCharm, Jupyter Notebook, etc.)
5. Development using Jupyter Notebook

5. Python Learning Resources

5.1 Official Documentation & Free Tutorials

Python Official Documentation（https://docs.python.org/ja/）

• Tutorial（basic syntax guide for beginners）
• Library Reference（detailed information on the standard library）
• Python Cookbook（practical code examples）

• Beginners should read the “Python Tutorial” and practice hands‑on
• Intermediate learners should refer to the “Library Reference” while implementing
• Advanced users can learn practical scripts from the “Cookbook”

5.2 Free Online Learning Sites

1. Progate（https://prog-8.com/）

• Target: Beginners
• Features: Slide‑based learning + code can be run in the browser
• Benefits: No installation required, easy to start

2. W3Schools（https://www.w3schools.com/python/）

• Target: Beginners to intermediate
• Features: Learn by writing code hands‑on
• Benefits: Concise explanations with sample code

3. Google Colab（https://colab.research.google.com/）

• Target: Those who want to learn data analysis and machine learning
• Features: Free access to a Jupyter Notebook environment
• Benefits: No need to install libraries, integrates with Google Drive

5.3 YouTube & Video Courses

1. Dot Install（https://dotinstall.com/）

• Target: Beginners
• Features: Learn the basics through short videos

2. Udemy（https://www.udemy.com/）

• Target: Beginners to advanced
• Features: Paid, but offers many high‑quality courses

3. YouTube (free courses)

• “Python Introduction”
• “Python Beginner Course”

5.4 Python Communities & Meetups

Sites for asking Python questions

Python Meetups & Events

5.6 Summary

1. Official Documentation – the most reliable reference for Python
2. Free Online Learning Sites – such as Progate, W3Schools, Google Colab, etc.
3. Recommended Books – books covering beginners to advanced
4. Video Courses – visual learning via YouTube and Udemy
5. Communities & Meetups – such as Stack Overflow, Qiita, PyCon Japan, etc.

6. FAQ (Frequently Asked Questions)

6.1 Is Python a beginner-friendly programming language? Explanation of reasons

• Simple syntax: Code is intuitive and easy to read
• Abundant learning resources: Plenty of beginner-friendly materials and tutorials
• High practicality: Usable in many fields such as data analysis, AI, and web development
• Easy installation: You can start developing right away

6.2 What are the differences between Python and other languages (Java, C++)?

6.3 How long does it take to learn Python?

• 1 hour per day → Basic proficiency in about 3 months
• 3 hours per day → Basic proficiency in about 1 month

6.4 What is the future outlook and demand for Python?

• Growth of AI & machine learning: Adopted by major companies like Google, Facebook
• Expansion of data science: Companies are increasingly emphasizing data analysis
• Web development staple technologies: Django and Flask are popular
• Automation (RPA): Contributes to corporate workflow efficiency

• Python engineer (Web development): 5 million–7 million yen
• Data scientist (Python-focused): 6 million–9 million yen
• AI engineer (Python-focused): 7 million–12 million yen

6.5 How to leverage Python in your job?

1. Master Python fundamentals (variables, functions, libraries)
2. Select a specialization (Web development, data analysis, AI, etc.)
3. Create a portfolio (publish projects on GitHub)
4. Apply for jobs (look for Python positions at companies)
5. Find freelance gigs (e.g., CloudWorks, Lancers, etc.)

6.6 Summary

1. Is Python the optimal language for beginners? → Yes, it’s simple and easy to learn
2. What are the differences from other languages? → Simpler and more versatile than Java or C++
3. How long does it take to learn Python? → Basic proficiency possible 1–3 months
4. What is Python’s future outlook? → Demand is growing in AI and data analytics
5. How to leverage Python at work? → Build a portfolio, apply for jobs, seek freelance projects