THE BASICS OF PYTHON - 拾光赋-拾光赋

THE BASICS OF PYTHON

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Python is a high-level, interpreted programming language known for its simplicity and versatility. Web development Data analysis Artificial intelligence Scientific computing Automation Etc, it is widely used due to its many applications. Its extensive standard library, simple syntax and dynamic typing have made it popular among new developers as well as experienced coder.

Setting up Python

To start using Python, first, we must install a Python interpreter and a text editor or IDE (Integrated Development Environment). Popular choices include PyCharm, Visual Studio Code, and Spyder.

Download Python:
- Go to the official Python website: python.org
- Navigate to the Downloads section and choose the version suitable for your operating system (Windows, macOS, Linux).
Install Python:
- Run the installer.
- Make sure to check the option “Add Python to PATH” during the installation process.
- Follow the installation prompts.
Install a Code Editor
While you can write Python code in any text editor, using an Integrated Development Environment (IDE) or a code editor with Python support can greatly enhance your productivity. Here are some popular choices:
- VS Code (Visual Studio Code): A lightweight but powerful source code editor with support for Python.
- PyCharm: A full-featured IDE specifically for Python development.
- Sublime Text: A sophisticated text editor for code, markup, and prose.
Install a Virtual Environment
Creating a virtual environment helps manage dependencies and avoid conflicts between different projects.
- Create a Virtual Environment:
  - Open a terminal or command prompt.
  - Navigate to your project directory.
  - Run the command: python -m venv env
    - This creates a virtual environment named env.
- Activate the Virtual Environment:
  - On Windows: .\env\Scripts\activate
  - On macOS/Linux: source env/bin/activate
  - You should see (env) or similar in your terminal prompt indicating the virtual environment is active.
Write and Run a Simple Python Script
- Create a Python File:
- Open your code editor.
- Create a new file named hello.py.
- Write Some Code:
- Add the following code to hello.py:


<span>print</span><span>(</span><span>"</span><span>Hello, World!</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Hello, World!</span><span>"</span><span>)</span>
print("Hello, World!")

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Run the Script:
- Open a terminal or command prompt.
- Navigate to the directory containing hello.py.
- Run the script using: python hello.py

To start coding in Python, you must install a Python interpreter and a text editor or IDE (Integrated Development Environment). Popular choices include PyCharm, Visual Studio Code, and Spyder.

Basic Syntax
Python’s syntax is concise and easy to learn. It uses indentation to define code blocks instead of curly braces or keywords. Variables are assigned using the assignment operator (=).

Example:


<span>x</span> <span>=</span> <span>5</span>  <span># assign 5 to variable x </span><span>y</span> <span>=</span> <span>"</span><span>Hello</span><span>"</span>  <span># assign string "Hello" to variable y </span>
<span>x</span> <span>=</span> <span>5</span>  <span># assign 5 to variable x </span><span>y</span> <span>=</span> <span>"</span><span>Hello</span><span>"</span>  <span># assign string "Hello" to variable y </span>
x = 5  # assign 5 to variable x y = "Hello"  # assign string "Hello" to variable y

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Data Types
Python has built-in support for various data types, including:

Integers (int): whole numbers
Floats (float): decimal numbers
Strings (str): sequences of characters
Boolean (bool): True or False values
Lists (list): ordered collections of items

Example:


<span>my_list</span> <span>=</span> <span>[</span><span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>,</span> <span>"</span><span>four</span><span>"</span><span>,</span> <span>5.5</span><span>]</span>  <span># create a list with mixed data types </span>
<span>my_list</span> <span>=</span> <span>[</span><span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>,</span> <span>"</span><span>four</span><span>"</span><span>,</span> <span>5.5</span><span>]</span>  <span># create a list with mixed data types </span>
my_list = [1, 2, 3, "four", 5.5]  # create a list with mixed data types

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Operators and Control Structures

Python supports various operators for arithmetic, comparison, logical operations, and more. Control structures like if-else statements and for loops are used for decision-making and iteration.

Example:


<span>x</span> <span>=</span> <span>5</span>
<span>if</span> <span>x</span> <span>></span> <span>10</span><span>:</span>
    <span>print</span><span>(</span><span>"</span><span>x is greater than 10</span><span>"</span><span>)</span>
<span>else</span><span>:</span>
    <span>print</span><span>(</span><span>"</span><span>x is less than or equal to 10</span><span>"</span><span>)</span>
<span>for</span> <span>i</span> <span>in</span> <span>range</span><span>(</span><span>5</span><span>):</span>
    <span>print</span><span>(</span><span>i</span><span>)</span>  <span># prints numbers from 0 to 4 </span>
<span>x</span> <span>=</span> <span>5</span>
<span>if</span> <span>x</span> <span>></span> <span>10</span><span>:</span>
    <span>print</span><span>(</span><span>"</span><span>x is greater than 10</span><span>"</span><span>)</span>
<span>else</span><span>:</span>
    <span>print</span><span>(</span><span>"</span><span>x is less than or equal to 10</span><span>"</span><span>)</span>

<span>for</span> <span>i</span> <span>in</span> <span>range</span><span>(</span><span>5</span><span>):</span>
    <span>print</span><span>(</span><span>i</span><span>)</span>  <span># prints numbers from 0 to 4 </span>
x = 5
if x > 10:
    print("x is greater than 10")
else:
    print("x is less than or equal to 10")

for i in range(5):
    print(i)  # prints numbers from 0 to 4

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Functions

Functions are reusable blocks of code that take arguments and return values. They help organize code and reduce duplication.

Example:


<span>def</span> <span>greet</span><span>(</span><span>name</span><span>):</span>
    <span>print</span><span>(</span><span>"</span><span>Hello, </span><span>"</span> <span>+</span> <span>name</span> <span>+</span> <span>"</span><span>!</span><span>"</span><span>)</span>
<span>greet</span><span>(</span><span>"</span><span>John</span><span>"</span><span>)</span>  <span># outputs "Hello, John!" </span>
<span>def</span> <span>greet</span><span>(</span><span>name</span><span>):</span>
    <span>print</span><span>(</span><span>"</span><span>Hello, </span><span>"</span> <span>+</span> <span>name</span> <span>+</span> <span>"</span><span>!</span><span>"</span><span>)</span>

<span>greet</span><span>(</span><span>"</span><span>John</span><span>"</span><span>)</span>  <span># outputs "Hello, John!" </span>
def greet(name):
    print("Hello, " + name + "!")

greet("John")  # outputs "Hello, John!"

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Modules and Packages

Python has a vast collection of libraries and modules for various tasks, such as math, file I/O, and networking. You can import modules using the import statement.

Example:


<span>import</span> <span>math</span>
<span>print</span><span>(</span><span>math</span><span>.</span><span>pi</span><span>)</span>  <span># outputs the value of pi </span>
<span>import</span> <span>math</span>
<span>print</span><span>(</span><span>math</span><span>.</span><span>pi</span><span>)</span>  <span># outputs the value of pi </span>
import math
print(math.pi)  # outputs the value of pi

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File Input/Output

Python provides various ways to read and write files, including text files, CSV files, and more.

Example:


<span>with</span> <span>open</span><span>(</span><span>"</span><span>example.txt</span><span>"</span><span>,</span> <span>"</span><span>w</span><span>"</span><span>)</span> <span>as</span> <span>file</span><span>:</span>
    <span>file</span><span>.</span><span>write</span><span>(</span><span>"</span><span>This is an example text file.</span><span>"</span><span>)</span>
<span>with</span> <span>open</span><span>(</span><span>"</span><span>example.txt</span><span>"</span><span>,</span> <span>"</span><span>w</span><span>"</span><span>)</span> <span>as</span> <span>file</span><span>:</span>
    <span>file</span><span>.</span><span>write</span><span>(</span><span>"</span><span>This is an example text file.</span><span>"</span><span>)</span>
with open("example.txt", "w") as file:
    file.write("This is an example text file.")

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Exception Handling

Python uses try-except blocks to handle errors and exceptions gracefully.

Example:


<span>try</span><span>:</span>
    <span>x</span> <span>=</span> <span>5</span> <span>/</span> <span>0</span>
<span>except</span> <span>ZeroDivisionError</span><span>:</span>
    <span>print</span><span>(</span><span>"</span><span>Cannot divide by zero!</span><span>"</span><span>)</span> 
<span>try</span><span>:</span>
    <span>x</span> <span>=</span> <span>5</span> <span>/</span> <span>0</span>
<span>except</span> <span>ZeroDivisionError</span><span>:</span>
    <span>print</span><span>(</span><span>"</span><span>Cannot divide by zero!</span><span>"</span><span>)</span> 
try:
    x = 5 / 0
except ZeroDivisionError:
    print("Cannot divide by zero!")

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Object-Oriented Programming

Python supports object-oriented programming (OOP) concepts like classes, objects, inheritance, and polymorphism.

Example:


<span>class</span> <span>Person</span><span>:</span>
    <span>def</span> <span>__init__</span><span>(</span><span>self</span><span>,</span> <span>name</span><span>,</span> <span>age</span><span>):</span>
        <span>self</span><span>.</span><span>name</span> <span>=</span> <span>name</span>
        <span>self</span><span>.</span><span>age</span> <span>=</span> <span>age</span>
    <span>def</span> <span>greet</span><span>(</span><span>self</span><span>):</span>
        <span>print</span><span>(</span><span>"</span><span>Hello, my name is </span><span>"</span> <span>+</span> <span>self</span><span>.</span><span>name</span> <span>+</span> <span>"</span><span> and I am </span><span>"</span> <span>+</span> <span>str</span><span>(</span><span>self</span><span>.</span><span>age</span><span>)</span> <span>+</span> <span>"</span><span> years old.</span><span>"</span><span>)</span>
<span>person</span> <span>=</span> <span>Person</span><span>(</span><span>"</span><span>John</span><span>"</span><span>,</span> <span>30</span><span>)</span>
<span>person</span><span>.</span><span>greet</span><span>()</span>  <span># outputs "Hello, my name is John and I am 30 years old." </span>
<span>class</span> <span>Person</span><span>:</span>
    <span>def</span> <span>__init__</span><span>(</span><span>self</span><span>,</span> <span>name</span><span>,</span> <span>age</span><span>):</span>
        <span>self</span><span>.</span><span>name</span> <span>=</span> <span>name</span>
        <span>self</span><span>.</span><span>age</span> <span>=</span> <span>age</span>

    <span>def</span> <span>greet</span><span>(</span><span>self</span><span>):</span>
        <span>print</span><span>(</span><span>"</span><span>Hello, my name is </span><span>"</span> <span>+</span> <span>self</span><span>.</span><span>name</span> <span>+</span> <span>"</span><span> and I am </span><span>"</span> <span>+</span> <span>str</span><span>(</span><span>self</span><span>.</span><span>age</span><span>)</span> <span>+</span> <span>"</span><span> years old.</span><span>"</span><span>)</span>

<span>person</span> <span>=</span> <span>Person</span><span>(</span><span>"</span><span>John</span><span>"</span><span>,</span> <span>30</span><span>)</span>
<span>person</span><span>.</span><span>greet</span><span>()</span>  <span># outputs "Hello, my name is John and I am 30 years old." </span>
class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def greet(self):
        print("Hello, my name is " + self.name + " and I am " + str(self.age) + " years old.")

person = Person("John", 30)
person.greet()  # outputs "Hello, my name is John and I am 30 years old."

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Advanced Topics

Python has many advanced features, including generators, decorators, and asynchronous programming.

Example:


<span>def</span> <span>infinite_sequence</span><span>():</span>
    <span>num</span> <span>=</span> <span>0</span>
    <span>while</span> <span>True</span><span>:</span>
        <span>yield</span> <span>num</span>
        <span>num</span> <span>+=</span> <span>1</span>
<span>seq</span> <span>=</span> <span>infinite_sequence</span><span>()</span>
<span>for</span> <span>_</span> <span>in</span> <span>range</span><span>(</span><span>10</span><span>):</span>
    <span>print</span><span>(</span><span>next</span><span>(</span><span>seq</span><span>))</span>  <span># prints numbers from 0 to 9 </span>
<span>def</span> <span>infinite_sequence</span><span>():</span>
    <span>num</span> <span>=</span> <span>0</span>
    <span>while</span> <span>True</span><span>:</span>
        <span>yield</span> <span>num</span>
        <span>num</span> <span>+=</span> <span>1</span>

<span>seq</span> <span>=</span> <span>infinite_sequence</span><span>()</span>
<span>for</span> <span>_</span> <span>in</span> <span>range</span><span>(</span><span>10</span><span>):</span>
    <span>print</span><span>(</span><span>next</span><span>(</span><span>seq</span><span>))</span>  <span># prints numbers from 0 to 9 </span>
def infinite_sequence():
    num = 0
    while True:
        yield num
        num += 1

seq = infinite_sequence()
for _ in range(10):
    print(next(seq))  # prints numbers from 0 to 9

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Decorators

Decorators are a special type of function that can modify or extend the behavior of another function. They are denoted by the @ symbol followed by the decorator’s name.

Example:


<span>def</span> <span>my_decorator</span><span>(</span><span>func</span><span>):</span>
    <span>def</span> <span>wrapper</span><span>():</span>
        <span>print</span><span>(</span><span>"</span><span>Something is happening before the function is called.</span><span>"</span><span>)</span>
        <span>func</span><span>()</span>
        <span>print</span><span>(</span><span>"</span><span>Something is happening after the function is called.</span><span>"</span><span>)</span>
    <span>return</span> <span>wrapper</span>
<span>@my_decorator</span>
<span>def</span> <span>say_hello</span><span>():</span>
    <span>print</span><span>(</span><span>"</span><span>Hello!</span><span>"</span><span>)</span>
<span>say_hello</span><span>()</span>
<span>def</span> <span>my_decorator</span><span>(</span><span>func</span><span>):</span>
    <span>def</span> <span>wrapper</span><span>():</span>
        <span>print</span><span>(</span><span>"</span><span>Something is happening before the function is called.</span><span>"</span><span>)</span>
        <span>func</span><span>()</span>
        <span>print</span><span>(</span><span>"</span><span>Something is happening after the function is called.</span><span>"</span><span>)</span>
    <span>return</span> <span>wrapper</span>

<span>@my_decorator</span>
<span>def</span> <span>say_hello</span><span>():</span>
    <span>print</span><span>(</span><span>"</span><span>Hello!</span><span>"</span><span>)</span>

<span>say_hello</span><span>()</span>
def my_decorator(func):
    def wrapper():
        print("Something is happening before the function is called.")
        func()
        print("Something is happening after the function is called.")
    return wrapper

@my_decorator
def say_hello():
    print("Hello!")

say_hello()

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Generators

Generators are a type of iterable, like lists or tuples, but they generate their values on the fly instead of storing them in memory.

Example:


<span>def</span> <span>infinite_sequence</span><span>():</span>
    <span>num</span> <span>=</span> <span>0</span>
    <span>while</span> <span>True</span><span>:</span>
        <span>yield</span> <span>num</span>
        <span>num</span> <span>+=</span> <span>1</span>
<span>seq</span> <span>=</span> <span>infinite_sequence</span><span>()</span>
<span>for</span> <span>_</span> <span>in</span> <span>range</span><span>(</span><span>10</span><span>):</span>
    <span>print</span><span>(</span><span>next</span><span>(</span><span>seq</span><span>))</span>  <span># prints numbers from 0 to 9 </span>
<span>def</span> <span>infinite_sequence</span><span>():</span>
    <span>num</span> <span>=</span> <span>0</span>
    <span>while</span> <span>True</span><span>:</span>
        <span>yield</span> <span>num</span>
        <span>num</span> <span>+=</span> <span>1</span>

<span>seq</span> <span>=</span> <span>infinite_sequence</span><span>()</span>
<span>for</span> <span>_</span> <span>in</span> <span>range</span><span>(</span><span>10</span><span>):</span>
    <span>print</span><span>(</span><span>next</span><span>(</span><span>seq</span><span>))</span>  <span># prints numbers from 0 to 9 </span>
def infinite_sequence():
    num = 0
    while True:
        yield num
        num += 1

seq = infinite_sequence()
for _ in range(10):
    print(next(seq))  # prints numbers from 0 to 9

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Asyncio

Asyncio is a library for writing single-threaded concurrent code using coroutines, multiplexing I/O access over sockets and other resources, and implementing network clients and servers.

Example:


<span>import</span> <span>asyncio</span>
<span>async</span> <span>def</span> <span>my_function</span><span>():</span>
    <span>await</span> <span>asyncio</span><span>.</span><span>sleep</span><span>(</span><span>1</span><span>)</span>
    <span>print</span><span>(</span><span>"</span><span>Hello!</span><span>"</span><span>)</span>
<span>asyncio</span><span>.</span><span>run</span><span>(</span><span>my_function</span><span>())</span>
<span>import</span> <span>asyncio</span>

<span>async</span> <span>def</span> <span>my_function</span><span>():</span>
    <span>await</span> <span>asyncio</span><span>.</span><span>sleep</span><span>(</span><span>1</span><span>)</span>
    <span>print</span><span>(</span><span>"</span><span>Hello!</span><span>"</span><span>)</span>

<span>asyncio</span><span>.</span><span>run</span><span>(</span><span>my_function</span><span>())</span>
import asyncio

async def my_function():
    await asyncio.sleep(1)
    print("Hello!")

asyncio.run(my_function())

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Data Structures

Python has a range of built-in data structures, including lists, tuples, dictionaries, sets, and more. It also has libraries like NumPy and Pandas for efficient numerical and data analysis.

Example:


<span>import</span> <span>numpy</span> <span>as</span> <span>np</span>
<span>my_array</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([</span><span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>,</span> <span>4</span><span>,</span> <span>5</span><span>])</span>
<span>print</span><span>(</span><span>my_array</span> <span>*</span> <span>2</span><span>)</span>  <span># prints [2, 4, 6, 8, 10] </span>
<span>import</span> <span>numpy</span> <span>as</span> <span>np</span>

<span>my_array</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([</span><span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>,</span> <span>4</span><span>,</span> <span>5</span><span>])</span>
<span>print</span><span>(</span><span>my_array</span> <span>*</span> <span>2</span><span>)</span>  <span># prints [2, 4, 6, 8, 10] </span>
import numpy as np

my_array = np.array([1, 2, 3, 4, 5])
print(my_array * 2)  # prints [2, 4, 6, 8, 10]

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Web Development

Python has popular frameworks like Django, Flask, and Pyramid for building web applications. It also has libraries like Requests and BeautifulSoup for web scraping and crawling.

Example:


<span>from</span> <span>flask</span> <span>import</span> <span>Flask</span><span>,</span> <span>request</span>
<span>app</span> <span>=</span> <span>Flask</span><span>(</span><span>__name__</span><span>)</span>
<span>@app.route</span><span>(</span><span>"</span><span>/</span><span>"</span><span>)</span>
<span>def</span> <span>hello</span><span>():</span>
    <span>return</span> <span>"</span><span>Hello, World!</span><span>"</span>
<span>if</span> <span>__name__</span> <span>==</span> <span>"</span><span>__main__</span><span>"</span><span>:</span>
    <span>app</span><span>.</span><span>run</span><span>()</span>
<span>from</span> <span>flask</span> <span>import</span> <span>Flask</span><span>,</span> <span>request</span>

<span>app</span> <span>=</span> <span>Flask</span><span>(</span><span>__name__</span><span>)</span>

<span>@app.route</span><span>(</span><span>"</span><span>/</span><span>"</span><span>)</span>
<span>def</span> <span>hello</span><span>():</span>
    <span>return</span> <span>"</span><span>Hello, World!</span><span>"</span>

<span>if</span> <span>__name__</span> <span>==</span> <span>"</span><span>__main__</span><span>"</span><span>:</span>
    <span>app</span><span>.</span><span>run</span><span>()</span>
from flask import Flask, request

app = Flask(__name__)

@app.route("/")
def hello():
    return "Hello, World!"

if __name__ == "__main__":
    app.run()

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Data Analysis

Python has libraries like Pandas, NumPy, and Matplotlib for data analysis and visualization. It also has Scikit-learn for machine learning tasks.

Example:


<span>import</span> <span>pandas</span> <span>as</span> <span>pd</span>
<span>import</span> <span>matplotlib.pyplot</span> <span>as</span> <span>plt</span>
<span>data</span> <span>=</span> <span>pd</span><span>.</span><span>read_csv</span><span>(</span><span>"</span><span>my_data.csv</span><span>"</span><span>)</span>
<span>plt</span><span>.</span><span>plot</span><span>(</span><span>data</span><span>[</span><span>"</span><span>column1</span><span>"</span><span>])</span>
<span>plt</span><span>.</span><span>show</span><span>()</span>
<span>import</span> <span>pandas</span> <span>as</span> <span>pd</span>
<span>import</span> <span>matplotlib.pyplot</span> <span>as</span> <span>plt</span>

<span>data</span> <span>=</span> <span>pd</span><span>.</span><span>read_csv</span><span>(</span><span>"</span><span>my_data.csv</span><span>"</span><span>)</span>
<span>plt</span><span>.</span><span>plot</span><span>(</span><span>data</span><span>[</span><span>"</span><span>column1</span><span>"</span><span>])</span>
<span>plt</span><span>.</span><span>show</span><span>()</span>
import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv("my_data.csv")
plt.plot(data["column1"])
plt.show()

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Machine Learning

Python has libraries like Scikit-learn, TensorFlow, and Keras for building machine learning models. It also has libraries like NLTK and spaCy for natural language processing.

Example:


<span>from</span> <span>sklearn.linear_model</span> <span>import</span> <span>LinearRegression</span>
<span>from</span> <span>sklearn.datasets</span> <span>import</span> <span>load_boston</span>
<span>from</span> <span>sklearn.model_selection</span> <span>import</span> <span>train_test_split</span>
<span>boston_data</span> <span>=</span> <span>load_boston</span><span>()</span>
<span>X_train</span><span>,</span> <span>X_test</span><span>,</span> <span>y_train</span><span>,</span> <span>y_test</span> <span>=</span> <span>train_test_split</span><span>(</span><span>boston_data</span><span>.</span><span>data</span><span>,</span> <span>boston_data</span><span>.</span><span>target</span><span>,</span> <span>test_size</span><span>=</span><span>0.2</span><span>,</span> <span>random_state</span><span>=</span><span>0</span><span>)</span>
<span>model</span> <span>=</span> <span>LinearRegression</span><span>()</span>
<span>model</span><span>.</span><span>fit</span><span>(</span><span>X_train</span><span>,</span> <span>y_train</span><span>)</span>
<span>print</span><span>(</span><span>model</span><span>.</span><span>score</span><span>(</span><span>X_test</span><span>,</span> <span>y_test</span><span>))</span>  <span># prints the R^2 score of the model </span>
<span>from</span> <span>sklearn.linear_model</span> <span>import</span> <span>LinearRegression</span>
<span>from</span> <span>sklearn.datasets</span> <span>import</span> <span>load_boston</span>
<span>from</span> <span>sklearn.model_selection</span> <span>import</span> <span>train_test_split</span>

<span>boston_data</span> <span>=</span> <span>load_boston</span><span>()</span>
<span>X_train</span><span>,</span> <span>X_test</span><span>,</span> <span>y_train</span><span>,</span> <span>y_test</span> <span>=</span> <span>train_test_split</span><span>(</span><span>boston_data</span><span>.</span><span>data</span><span>,</span> <span>boston_data</span><span>.</span><span>target</span><span>,</span> <span>test_size</span><span>=</span><span>0.2</span><span>,</span> <span>random_state</span><span>=</span><span>0</span><span>)</span>
<span>model</span> <span>=</span> <span>LinearRegression</span><span>()</span>
<span>model</span><span>.</span><span>fit</span><span>(</span><span>X_train</span><span>,</span> <span>y_train</span><span>)</span>
<span>print</span><span>(</span><span>model</span><span>.</span><span>score</span><span>(</span><span>X_test</span><span>,</span> <span>y_test</span><span>))</span>  <span># prints the R^2 score of the model </span>
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

boston_data = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)
model = LinearRegression()
model.fit(X_train, y_train)
print(model.score(X_test, y_test))  # prints the R^2 score of the model

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Conclusion

Python is a versatile language with a wide range of applications, from web development to data analysis and machine learning. Its simplicity, readability, and large community make it an ideal language for beginners and experienced programmers alike.

原文链接：THE BASICS OF PYTHON

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