網(wǎng)站推廣有哪些公司可以做,東莞seo推廣,一個微信小程序需要多少錢,應(yīng)用軟件開發(fā)過程Python 全面深入解析#xff1a;從入門到精通第一部分#xff1a;Python 語言概述與哲學(xué)1.1 Python 的誕生與發(fā)展Python 由吉多范羅蘇姆#xff08;Guido van Rossum#xff09;于1989年圣誕節(jié)期間在荷蘭創(chuàng)建#xff0c;最初作為 ABC 語言的替代品。1991年#xff0c;Pyt…Python 全面深入解析從入門到精通第一部分Python 語言概述與哲學(xué)1.1 Python 的誕生與發(fā)展Python 由吉多·范羅蘇姆Guido van Rossum于1989年圣誕節(jié)期間在荷蘭創(chuàng)建最初作為 ABC 語言的替代品。1991年P(guān)ython 0.9.0 首次公開發(fā)布。Python 的名字并非來自蟒蛇而是取自英國喜劇團體 Monty Python 的飛行馬戲團。歷史里程碑1994年P(guān)ython 1.0 發(fā)布包含函數(shù)式編程工具2000年P(guān)ython 2.0 發(fā)布引入垃圾回收和 Unicode 支持2008年P(guān)ython 3.0 發(fā)布解決2.x系列的設(shè)計缺陷2020年P(guān)ython 2.x 正式停止維護1.2 Python 的設(shè)計哲學(xué)Python 遵循 Zen of PythonPython之禪的設(shè)計原則pythonimport this # 輸出 Python 之禪核心原則包括優(yōu)美勝于丑陋明了勝于晦澀簡單勝于復(fù)雜可讀性很重要實用性勝過純粹性1.3 Python 的特點與優(yōu)勢簡潔優(yōu)雅的語法使用縮進代替花括號代碼結(jié)構(gòu)清晰動態(tài)類型系統(tǒng)變量無需聲明類型類型在運行時確定解釋型語言無需編譯直接解釋執(zhí)行跨平臺性支持 Windows、Linux、macOS 等操作系統(tǒng)豐富的標(biāo)準(zhǔn)庫包含200多個核心模塊強大的生態(tài)系統(tǒng)超過30萬個第三方包多范式支持支持面向?qū)ο?、函?shù)式、過程式編程內(nèi)存管理自動垃圾回收機制第二部分Python 核心語法詳解2.1 基礎(chǔ)語法結(jié)構(gòu)2.1.1 變量與數(shù)據(jù)類型python# 基本數(shù)據(jù)類型 # 整數(shù) x 10 # 浮點數(shù) y 3.14 # 復(fù)數(shù) z 2 3j # 布爾值 is_true True is_false False # 字符串 name Python # None 類型 empty None # 類型檢查與轉(zhuǎn)換 print(type(x)) # class int print(isinstance(y, float)) # True print(str(x) 10) # 類型轉(zhuǎn)換2.1.2 數(shù)據(jù)結(jié)構(gòu)列表Listpython# 列表創(chuàng)建與操作 fruits [apple, banana, cherry] numbers [1, 2, 3, 4, 5] # 列表推導(dǎo)式 squares [x**2 for x in range(10)] even_squares [x**2 for x in range(10) if x % 2 0] # 列表方法 fruits.append(orange) # 添加元素 fruits.insert(1, grape) # 插入元素 fruits.remove(banana) # 移除元素 last_fruit fruits.pop() # 彈出最后一個元素 fruits.sort() # 排序 fruits.reverse() # 反轉(zhuǎn)元組Tuplepython# 元組是不可變的 coordinates (10, 20) rgb (255, 128, 0) single_tuple (5,) # 單元素元組需要逗號 # 元組解包 x, y coordinates red, green, blue rgb # 命名元組 from collections import namedtuple Point namedtuple(Point, [x, y]) p Point(10, 20) print(p.x, p.y)字典Dictionarypython# 字典創(chuàng)建與操作 person { name: Alice, age: 30, city: New York } # 字典推導(dǎo)式 square_dict {x: x**2 for x in range(5)} # 字典方法 print(person.get(name)) # 安全獲取 print(person.keys()) # 所有鍵 print(person.values()) # 所有值 print(person.items()) # 所有鍵值對 # 合并字典Python 3.9 dict1 {a: 1, b: 2} dict2 {b: 3, c: 4} merged dict1 | dict2 # {a: 1, b: 3, c: 4}集合Setpython# 集合操作 set1 {1, 2, 3, 4, 5} set2 {4, 5, 6, 7, 8} # 集合運算 union set1 | set2 # 并集 intersection set1 set2 # 交集 difference set1 - set2 # 差集 symmetric_diff set1 ^ set2 # 對稱差集 # 集合推導(dǎo)式 even_set {x for x in range(10) if x % 2 0}2.2 控制結(jié)構(gòu)2.2.1 條件語句python# if-elif-else 結(jié)構(gòu) score 85 if score 90: grade A elif score 80: grade B elif score 70: grade C else: grade F # 三元表達(dá)式 status 及格 if score 60 else 不及格 # 模式匹配Python 3.10 def handle_command(command): match command.split(): case [quit]: print(退出程序) case [load, filename]: print(f加載文件: {filename}) case [save, filename]: print(f保存文件: {filename}) case _: print(未知命令)2.2.2 循環(huán)結(jié)構(gòu)python# for 循環(huán) for i in range(5): # 0, 1, 2, 3, 4 print(i) for index, value in enumerate([a, b, c]): print(index, value) # while 循環(huán) count 0 while count 5: print(count) count 1 # 循環(huán)控制 for i in range(10): if i 3: continue # 跳過本次迭代 if i 7: break # 退出循環(huán) print(i) else: print(循環(huán)正常結(jié)束) # 循環(huán)未被break中斷時執(zhí)行2.3 函數(shù)編程2.3.1 函數(shù)定義與使用python# 基本函數(shù)定義 def greet(name, greetingHello): 返回問候語 return f{greeting}, {name}! # 函數(shù)調(diào)用 print(greet(Alice)) # Hello, Alice! print(greet(Bob, Hi)) # Hi, Bob! # 可變參數(shù) def sum_all(*args): 計算所有參數(shù)的和 return sum(args) print(sum_all(1, 2, 3, 4, 5)) # 15 # 關(guān)鍵字參數(shù) def print_info(**kwargs): for key, value in kwargs.items(): print(f{key}: {value}) print_info(nameAlice, age30, cityNYC) # 類型提示Python 3.5 def add(a: int, b: int) - int: 返回兩個整數(shù)的和 return a b # lambda 表達(dá)式 square lambda x: x ** 2 numbers [1, 2, 3, 4, 5] squared list(map(lambda x: x**2, numbers))2.3.2 高級函數(shù)特性python# 閉包 def make_multiplier(factor): def multiplier(x): return x * factor return multiplier double make_multiplier(2) triple make_multiplier(3) print(double(5)) # 10 print(triple(5)) # 15 # 裝飾器 def timer(func): import time def wrapper(*args, **kwargs): start time.time() result func(*args, **kwargs) end time.time() print(f{func.__name__} 執(zhí)行時間: {end-start:.4f}秒) return result return wrapper timer def slow_function(): import time time.sleep(1) return 完成 # 生成器 def fibonacci_generator(n): 生成斐波那契數(shù)列 a, b 0, 1 count 0 while count n: yield a a, b b, a b count 1 # 使用生成器 for num in fibonacci_generator(10): print(num)2.4 面向?qū)ο缶幊?.4.1 類與對象pythonclass Person: 人類 # 類屬性 species Homo sapiens def __init__(self, name, age): 初始化方法 self.name name # 實例屬性 self.age age def greet(self): 實例方法 return f你好我是{self.name}今年{self.age}歲 classmethod def create_baby(cls, name): 類方法 return cls(name, 0) staticmethod def is_adult(age): 靜態(tài)方法 return age 18 # 創(chuàng)建實例 alice Person(Alice, 30) print(alice.greet()) # 繼承 class Student(Person): def __init__(self, name, age, student_id): super().__init__(name, age) self.student_id student_id def study(self, subject): return f{self.name} 正在學(xué)習(xí) {subject} # 方法重寫 def greet(self): return f你好我是學(xué)生{self.name}學(xué)號{self.student_id} # 多態(tài) def introduce(person): print(person.greet()) bob Student(Bob, 20, S12345) introduce(bob)2.4.2 特殊方法與屬性pythonclass Vector: 向量類 def __init__(self, x, y): self.x x self.y y # 字符串表示 def __str__(self): return fVector({self.x}, {self.y}) def __repr__(self): return fVector({self.x}, {self.y}) # 運算符重載 def __add__(self, other): return Vector(self.x other.x, self.y other.y) def __sub__(self, other): return Vector(self.x - other.x, self.y - other.y) def __mul__(self, scalar): return Vector(self.x * scalar, self.y * scalar) def __eq__(self, other): return self.x other.x and self.y other.y # 長度 def __len__(self): return 2 # 迭代支持 def __iter__(self): return iter((self.x, self.y)) # 上下文管理器 def __enter__(self): print(進入上下文) return self def __exit__(self, exc_type, exc_val, exc_tb): print(退出上下文) # 使用向量 v1 Vector(1, 2) v2 Vector(3, 4) v3 v1 v2 print(v3) # Vector(4, 6) # 上下文管理器使用 with Vector(1, 2) as v: print(v)第三部分Python 高級特性3.1 內(nèi)存管理與垃圾回收Python 使用自動垃圾回收機制主要基于引用計數(shù)和分代回收pythonimport sys import gc class MemoryDemo: def __init__(self, name): self.name name print(f{self.name} 被創(chuàng)建) def __del__(self): print(f{self.name} 被銷毀) # 引用計數(shù) obj1 MemoryDemo(對象1) print(f引用計數(shù): {sys.getrefcount(obj1)}) # 循環(huán)引用 obj2 MemoryDemo(對象2) obj3 MemoryDemo(對象3) obj2.other obj3 obj3.other obj2 # 手動垃圾回收 del obj2, obj3 gc.collect() # 手動觸發(fā)垃圾回收3.2 并發(fā)與并行編程3.2.1 多線程pythonimport threading import time from queue import Queue def worker(q, thread_id): 工作線程函數(shù) while True: task q.get() if task is None: break print(f線程 {thread_id} 處理任務(wù): {task}) time.sleep(0.5) q.task_done() # 線程池 class ThreadPool: def __init__(self, num_threads): self.tasks Queue() self.threads [] for i in range(num_threads): thread threading.Thread(targetworker, args(self.tasks, i)) thread.start() self.threads.append(thread) def add_task(self, task): self.tasks.put(task) def wait_completion(self): self.tasks.join() for _ in self.threads: self.tasks.put(None) for thread in self.threads: thread.join() # 使用線程池 pool ThreadPool(3) for i in range(10): pool.add_task(f任務(wù){(diào)i}) pool.wait_completion()3.2.2 多進程pythonimport multiprocessing import time def cpu_intensive_task(n): CPU密集型任務(wù) result 0 for i in range(n): result i ** 2 return result if __name__ __main__: # 進程池 with multiprocessing.Pool(processes4) as pool: # 同步執(zhí)行 result pool.apply(cpu_intensive_task, (1000000,)) print(f同步結(jié)果: {result}) # 異步執(zhí)行 async_results [ pool.apply_async(cpu_intensive_task, (i,)) for i in [100000, 200000, 300000] ] # 獲取結(jié)果 for res in async_results: print(f異步結(jié)果: {res.get()})3.2.3 異步編程pythonimport asyncio import aiohttp async def fetch_url(session, url): 異步獲取URL內(nèi)容 async with session.get(url) as response: return await response.text() async def main(): urls [ https://www.python.org, https://www.github.com, https://www.stackoverflow.com ] async with aiohttp.ClientSession() as session: tasks [fetch_url(session, url) for url in urls] results await asyncio.gather(*tasks) for url, content in zip(urls, results): print(f{url}: {len(content)} 字符) # 運行異步程序 asyncio.run(main())3.3 元編程3.3.1 裝飾器高級用法python# 帶參數(shù)的裝飾器 def repeat(num_times): def decorator_repeat(func): def wrapper(*args, **kwargs): for _ in range(num_times): result func(*args, **kwargs) return result return wrapper return decorator_repeat repeat(num_times3) def say_hello(name): print(fHello, {name}!) say_hello(Alice) # 類裝飾器 class CountCalls: def __init__(self, func): self.func func self.num_calls 0 def __call__(self, *args, **kwargs): self.num_calls 1 print(f調(diào)用次數(shù): {self.num_calls}) return self.func(*args, **kwargs) CountCalls def say_hi(): print(Hi!) for _ in range(3): say_hi()3.3.2 元類python# 自定義元類 class SingletonMeta(type): 單例元類 _instances {} def __call__(cls, *args, **kwargs): if cls not in cls._instances: cls._instances[cls] super().__call__(*args, **kwargs) return cls._instances[cls] class Database(metaclassSingletonMeta): def __init__(self): print(數(shù)據(jù)庫連接初始化) # 驗證單例 db1 Database() db2 Database() print(fdb1 is db2: {db1 is db2}) # True # 動態(tài)創(chuàng)建類 def create_class(class_name, base_classes, attributes): return type(class_name, base_classes, attributes) # 使用 type 動態(tài)創(chuàng)建類 Animal create_class(Animal, (), {name: Unknown, sound: ...}) class Dog(Animal): def bark(self): return f{self.name} says: Woof! dog Dog() dog.name Buddy print(dog.bark())第四部分Python 標(biāo)準(zhǔn)庫詳解4.1 常用內(nèi)置模塊4.1.1 collections 模塊pythonfrom collections import defaultdict, Counter, deque, OrderedDict # defaultdict word_counts defaultdict(int) for word in [apple, banana, apple, cherry]: word_counts[word] 1 # Counter counter Counter([apple, banana, apple, cherry]) print(counter.most_common(2)) # deque queue deque([a, b, c]) queue.append(d) # 右端添加 queue.appendleft(z) # 左端添加 queue.pop() # 右端彈出 queue.popleft() # 左端彈出 # OrderedDict ordered_dict OrderedDict() ordered_dict[first] 1 ordered_dict[second] 2 ordered_dict[third] 34.1.2 itertools 模塊pythonimport itertools # 無限迭代器 counter itertools.count(start10, step2) cycle itertools.cycle([A, B, C]) repeat itertools.repeat(10, times3) # 組合迭代器 # 排列 perms itertools.permutations(ABC, 2) # 組合 combs itertools.combinations(ABCD, 2) # 笛卡爾積 product itertools.product(AB, 12) # 分組 data [(a, 1), (a, 2), (b, 3), (b, 4)] for key, group in itertools.groupby(data, lambda x: x[0]): print(key, list(group))4.2 文件與IO操作pythonimport os import json import pickle import csv # 文件操作 with open(example.txt, w, encodingutf-8) as f: f.write(Hello, Python! ) f.write(這是第二行) with open(example.txt, r, encodingutf-8) as f: content f.read() print(content) # JSON 序列化 data { name: Alice, age: 30, skills: [Python, JavaScript] } with open(data.json, w) as f: json.dump(data, f, indent2) with open(data.json, r) as f: loaded_data json.load(f) # CSV 操作 with open(data.csv, w, newline) as f: writer csv.writer(f) writer.writerow([Name, Age, City]) writer.writerow([Alice, 30, New York]) writer.writerow([Bob, 25, London]) with open(data.csv, r) as f: reader csv.DictReader(f) for row in reader: print(row)第五部分Python 應(yīng)用領(lǐng)域與框架5.1 Web 開發(fā)5.1.1 Flask 框架pythonfrom flask import Flask, request, jsonify, render_template app Flask(__name__) app.route(/) def home(): return render_template(index.html) app.route(/api/users, methods[GET, POST]) def users(): if request.method POST: data request.json # 處理用戶創(chuàng)建邏輯 return jsonify({message: 用戶創(chuàng)建成功, data: data}), 201 else: # 返回用戶列表 return jsonify({users: [{id: 1, name: Alice}]}) app.route(/api/users/int:user_id, methods[GET, PUT, DELETE]) def user_detail(user_id): if request.method GET: return jsonify({id: user_id, name: Alice}) elif request.method PUT: data request.json return jsonify({message: 用戶更新成功, data: data}) elif request.method DELETE: return jsonify({message: 用戶刪除成功}), 204 if __name__ __main__: app.run(debugTrue)5.1.2 Django 框架python# models.py from django.db import models class Product(models.Model): name models.CharField(max_length100) price models.DecimalField(max_digits10, decimal_places2) description models.TextField() created_at models.DateTimeField(auto_now_addTrue) def __str__(self): return self.name # views.py from django.shortcuts import render, get_object_or_404 from django.http import JsonResponse from .models import Product def product_list(request): products Product.objects.all() return render(request, products/list.html, {products: products}) def product_detail(request, product_id): product get_object_or_404(Product, idproduct_id) return render(request, products/detail.html, {product: product}) # API view from rest_framework import viewsets from .serializers import ProductSerializer class ProductViewSet(viewsets.ModelViewSet): queryset Product.objects.all() serializer_class ProductSerializer5.2 數(shù)據(jù)科學(xué)與機器學(xué)習(xí)5.2.1 NumPy 與 Pandaspythonimport numpy as np import pandas as pd # NumPy 數(shù)組操作 arr np.array([[1, 2, 3], [4, 5, 6]]) print(arr.shape) # (2, 3) print(arr.mean()) # 3.5 print(arr.sum(axis0)) # [5 7 9] # 線性代數(shù) A np.array([[1, 2], [3, 4]]) B np.array([[5, 6], [7, 8]]) print(np.dot(A, B)) # 矩陣乘法 # Pandas 數(shù)據(jù)分析 df pd.DataFrame({ Name: [Alice, Bob, Charlie], Age: [25, 30, 35], Salary: [50000, 60000, 70000] }) # 數(shù)據(jù)操作 print(df.describe()) print(df[df[Age] 28]) df[Bonus] df[Salary] * 0.1 # 分組聚合 grouped df.groupby(Age).agg({Salary: [mean, sum]})5.2.2 Scikit-learn 機器學(xué)習(xí)pythonfrom sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, classification_report # 加載數(shù)據(jù) iris datasets.load_iris() X iris.data y iris.target # 數(shù)據(jù)預(yù)處理 X_train, X_test, y_train, y_test train_test_split( X, y, test_size0.2, random_state42 ) scaler StandardScaler() X_train_scaled scaler.fit_transform(X_train) X_test_scaled scaler.transform(X_test) # 模型訓(xùn)練 model RandomForestClassifier(n_estimators100, random_state42) model.fit(X_train_scaled, y_train) # 預(yù)測與評估 y_pred model.predict(X_test_scaled) accuracy accuracy_score(y_test, y_pred) print(f準(zhǔn)確率: {accuracy:.2f}) print(classification_report(y_test, y_pred))5.3 自動化與腳本pythonimport os import shutil import subprocess import schedule import time # 文件系統(tǒng)操作 def organize_files(directory): 整理目錄中的文件 extensions { images: [.jpg, .png, .gif], documents: [.pdf, .docx, .txt], code: [.py, .js, .html] } for filename in os.listdir(directory): filepath os.path.join(directory, filename) if os.path.isfile(filepath): ext os.path.splitext(filename)[1].lower() for folder, ext_list in extensions.items(): if ext in ext_list: folder_path os.path.join(directory, folder) os.makedirs(folder_path, exist_okTrue) shutil.move(filepath, os.path.join(folder_path, filename)) break # 系統(tǒng)命令執(zhí)行 def run_command(cmd): result subprocess.run( cmd, shellTrue, capture_outputTrue, textTrue ) if result.returncode 0: return result.stdout else: return result.stderr # 定時任務(wù) def job(): print(執(zhí)行定時任務(wù)...) # 備份數(shù)據(jù)庫或發(fā)送報告等操作 # 設(shè)置定時任務(wù) schedule.every().day.at(10:30).do(job) schedule.every().hour.do(job) while True: schedule.run_pending() time.sleep(1)第六部分Python 最佳實踐與優(yōu)化6.1 代碼規(guī)范與風(fēng)格python 模塊文檔字符串 import os import sys from typing import List, Dict, Optional # 常量使用大寫 MAX_SIZE 100 DEFAULT_CONFIG {debug: False} class ExampleClass: 類文檔字符串 def __init__(self, name: str, value: int 0): self.name name self.value value self._private_attr 私有 # 單下劃線表示保護屬性 self.__mangled_attr 名稱修飾 # 雙下劃線觸發(fā)名稱修飾 def public_method(self) - str: 公共方法文檔字符串 return self._private_method() def _private_method(self) - str: 保護方法約定為私有 return fName: {self.name} staticmethod def static_example(): 靜態(tài)方法示例 return 靜態(tài)方法 classmethod def class_example(cls): 類方法示例 return f類名: {cls.__name__} def process_data(data: List[Dict]) - Optional[List[str]]: 處理數(shù)據(jù)函數(shù) Args: data: 輸入數(shù)據(jù)列表 Returns: 處理后的字符串列表如果出錯則返回None Raises: ValueError: 當(dāng)數(shù)據(jù)為空時 if not data: raise ValueError(數(shù)據(jù)不能為空) result [] for item in data: if isinstance(item, dict): result.append(str(item)) return result if result else None if __name__ __main__: # 主程序入口 example ExampleClass(測試) print(example.public_method())6.2 性能優(yōu)化技巧pythonimport timeit from functools import lru_cache from collections import defaultdict # 1. 使用局部變量 def optimized_sum(numbers): local_sum sum # 局部變量比全局變量快 return local_sum(numbers) # 2. 列表推導(dǎo)式 vs 循環(huán) def test_comprehension(): # 更快 squares [x**2 for x in range(1000)] return squares def test_loop(): # 較慢 squares [] for x in range(1000): squares.append(x**2) return squares # 3. 使用 join 連接字符串 def build_string_fast(items): return .join(str(item) for item in items) def build_string_slow(items): result for item in items: result str(item) return result.strip() # 4. 緩存計算結(jié)果 lru_cache(maxsize128) def fibonacci(n): if n 2: return n return fibonacci(n-1) fibonacci(n-2) # 5. 使用生成器節(jié)省內(nèi)存 def read_large_file(filename): with open(filename, r) as f: for line in f: yield line.strip() # 性能測試 if __name__ __main__: # 測試不同方法的性能 numbers list(range(1000)) print(列表推導(dǎo)式:, timeit.timeit(test_comprehension, number1000)) print(普通循環(huán):, timeit.timeit(test_loop, number1000)) # 測試斐波那契緩存 print(斐波那契(30)帶緩存:, timeit.timeit(lambda: fibonacci(30), number10))6.3 調(diào)試與測試pythonimport pdb import logging import unittest from unittest.mock import Mock, patch # 配置日志 logging.basicConfig( levellogging.DEBUG, format%(asctime)s - %(name)s - %(levelname)s - %(message)s ) logger logging.getLogger(__name__) def divide(a, b): 除法函數(shù) logger.debug(f計算 {a} / ) try: result a / b except ZeroDivisionError: logger.error(除數(shù)不能為零) raise except TypeError: logger.error(參數(shù)類型錯誤) raise else: logger.info(f計算結(jié)果: {result}) return result # 使用 pdb 調(diào)試 def debug_example(): x 10 y 0 # 設(shè)置斷點 pdb.set_trace() try: result divide(x, y) except Exception as e: print(f錯誤: {e}) # 單元測試 class TestMathFunctions(unittest.TestCase): def setUp(self): 測試前的準(zhǔn)備工作 self.numbers [1, 2, 3, 4, 5] def test_sum(self): 測試求和函數(shù) self.assertEqual(sum(self.numbers), 15) def test_divide(self): 測試除法函數(shù) self.assertEqual(divide(10, 2), 5) self.assertAlmostEqual(divide(1, 3), 0.333333, places6) def test_divide_by_zero(self): 測試除零異常 with self.assertRaises(ZeroDivisionError): divide(10, 0) patch(math.sqrt) def test_with_mock(self, mock_sqrt): 使用模擬對象測試 mock_sqrt.return_value 5.0 result divide(25, 5) mock_sqrt.assert_called_once_with(25) self.assertEqual(result, 5) # 運行測試 if __name__ __main__: unittest.main()第七部分Python 的未來與發(fā)展7.1 Python 的新特性Python 持續(xù)演進每個版本都帶來新的改進Python 3.8海象運算符:、位置參數(shù)、f-string 增強Python 3.9字典合并運算符、類型提示改進、字符串方法Python 3.10結(jié)構(gòu)模式匹配、更友好的錯誤提示Python 3.11顯著性能提升、異常組、類型系統(tǒng)增強7.2 Python 在人工智能領(lǐng)域的應(yīng)用python# 使用 PyTorch 進行深度學(xué)習(xí) import torch import torch.nn as nn import torch.optim as optim class NeuralNetwork(nn.Module): def __init__(self): super().__init__() self.flatten nn.Flatten() self.linear_relu_stack nn.Sequential( nn.Linear(28*28, 512), nn.ReLU(), nn.Linear(512, 512), nn.ReLU(), nn.Linear(512, 10) ) def forward(self, x): x self.flatten(x) logits self.linear_relu_stack(x) return logits # 訓(xùn)練循環(huán)示例 def train(model, dataloader, loss_fn, optimizer, epochs5): model.train() for epoch in range(epochs): for batch, (X, y) in enumerate(dataloader): # 前向傳播 pred model(X) loss loss_fn(pred, y) # 反向傳播 optimizer.zero_grad() loss.backward() optimizer.step()7.3 Python 生態(tài)系統(tǒng)的發(fā)展Python 生態(tài)系統(tǒng)持續(xù)壯大Web框架FastAPI高性能API框架、Streamlit數(shù)據(jù)應(yīng)用數(shù)據(jù)科學(xué)Polars高性能DataFrame、DuckDB嵌入式數(shù)據(jù)庫機器學(xué)習(xí)Hugging Face Transformers、LightGBM、XGBoost開發(fā)工具Poetry依賴管理、Ruff快速代碼檢查類型檢查Pyright、mypy異步生態(tài)Trio、anyio結(jié)語Python 作為一門多功能、易學(xué)易用的編程語言已經(jīng)成為軟件開發(fā)、數(shù)據(jù)分析、人工智能等領(lǐng)域的首選語言。它的成功不僅在于語法的簡潔優(yōu)雅更在于其龐大而活躍的社區(qū)、豐富的生態(tài)系統(tǒng)和持續(xù)創(chuàng)新的能力。從簡單的腳本到復(fù)雜的系統(tǒng)從數(shù)據(jù)分析到人工智能Python 都能提供高效、可靠的解決方案。隨著 Python 語言的持續(xù)發(fā)展和優(yōu)化它將繼續(xù)在技術(shù)領(lǐng)域發(fā)揮重要作用為開發(fā)者提供更強大的工具和更廣闊的可能性。無論是初學(xué)者還是經(jīng)驗豐富的開發(fā)者Python 都值得深入學(xué)習(xí)和掌握。通過理解 Python 的核心概念、掌握其高級特性、遵循最佳實踐開發(fā)者可以編寫出高效、可維護、優(yōu)雅的 Python 代碼解決現(xiàn)實世界中的各種挑戰(zhàn)。