【建议收藏】30个较难Python脚本，纯干货分享

本篇较难，建议优先学习上篇 ；20个硬核Python脚本-CSDN博客

接上篇文章，对于Pyhon的学习，上篇学习的结束相信大家对于Pyhon有了一定的理解和经验，学习完上篇文章之后再研究研究剩下的30个脚本你将会有所成就！加油！

目录

[21、数据库连接 - SQLite](#21、数据库连接 - SQLite)

[22、图像处理 - Pillow](#22、图像处理 - Pillow)

[23、图形界面 - Tkinter](#23、图形界面 - Tkinter)

[24、文本生成 - Faker](#24、文本生成 - Faker)

[25、加密和解密 - cryptography](#25、加密和解密 - cryptography)

26、Socket编程

[27、并发编程 - threading](#27、并发编程 - threading)

[28、正则表达式 - re](#28、正则表达式 - re)

[29、REST API - FastAPI](#29、REST API - FastAPI)

[30、数据库连接 - SQLAlchemy](#30、数据库连接 - SQLAlchemy)

[31、文本处理 - NLTK](#31、文本处理 - NLTK)

[32、命令行应用 - argparse](#32、命令行应用 - argparse)

[33、微服务 - Flask-RESTful](#33、微服务 - Flask-RESTful)

[34、数据处理 - BeautifulSoup](#34、数据处理 - BeautifulSoup)

[35、加密 - hashlib](#35、加密 - hashlib)

[36、数据序列化 - Pickle](#36、数据序列化 - Pickle)

[37、并行处理 - concurrent.futures](#37、并行处理 - concurrent.futures)

[38、网络爬虫 - Scrapy](#38、网络爬虫 - Scrapy)

[39、异步编程 - asyncio](#39、异步编程 - asyncio)

[40、数据分析 - Numpy](#40、数据分析 - Numpy)

[41、数据处理 - Pandas](#41、数据处理 - Pandas)

[42、数据可视化 - Matplotlib](#42、数据可视化 - Matplotlib)

[43、机器学习 - Scikit-Learn](#43、机器学习 - Scikit-Learn)

[44、机器学习 - Keras](#44、机器学习 - Keras)

[45、图像处理 - OpenCV](#45、图像处理 - OpenCV)

[46、数据爬取 - Scrapy](#46、数据爬取 - Scrapy)

[47、数据分析 - Seaborn](#47、数据分析 - Seaborn)

[48、数据可视化 - Plotly](#48、数据可视化 - Plotly)

[49、自然语言处理 - spaCy](#49、自然语言处理 - spaCy)

[50、机器学习 - XGBoost](#50、机器学习 - XGBoost)

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21、数据库连接 - SQLite

import sqlite3

conn = sqlite3.connect('mydatabase.db')
cursor = conn.cursor()
cursor.execute('CREATE TABLE IF NOT EXISTS users (id INTEGER PRIMARY KEY, name TEXT)')
conn.commit()
conn.close()

官方文档: https://www.sqlite.org/docs.html

22、图像处理 - Pillow

from PIL import Image

img = Image.open('example.jpg')
img.show()

官方文档: https://pillow.readthedocs.io/en/stable/index.html

23、图形界面 - Tkinter

import tkinter as tk

root = tk.Tk()
label = tk.Label(root, text="Hello, GUI!")
label.pack()
root.mainloop()

官方文档: https://docs.python.org/3/library/tkinter.html

24、文本生成 - Faker

from faker import Faker

fake = Faker()
print(fake.name())

官方文档: https://faker.readthedocs.io/en/master/

25、加密和解密 - cryptography

from cryptography.fernet import Fernet

key = Fernet.generate_key()
cipher_suite = Fernet(key)
text = "Secret message".encode()
cipher_text = cipher_suite.encrypt(text)
print(cipher_text)

官方文档: https://cryptography.io/en/latest/

26、Socket编程

import socket

server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_socket.bind(('127.0.0.1', 12345))
server_socket.listen(5)
print("Server is listening...")

while True:
    client_socket, addr = server_socket.accept()
    print(f"Connection from {addr}")
    client_socket.send(b"Hello, client!")
    client_socket.close()

官方文档: https://docs.python.org/3/library/socket.html

27、并发编程 - threading

import threading

def print

_numbers():
    for i in range(1, 6):
        print(f"Number: {i}")

def print_letters():
    for letter in "abcde":
        print(f"Letter: {letter}")

thread1 = threading.Thread(target=print_numbers)
thread2 = threading.Thread(target=print_letters)

thread1.start()
thread2.start()

官方文档: https://docs.python.org/3/library/threading.html

28、正则表达式 - re

import re

text = "My phone number is 123-456-7890."
pattern = r'\d{3}-\d{3}-\d{4}'
match = re.search(pattern, text)
if match:
    print(f"Phone number found: {match.group()}")

官方文档: https://docs.python.org/3/howto/regex.html

29、REST API - FastAPI

from fastapi import FastAPI

app = FastAPI()

@app.get("/items/{item_id}")
def read_item(item_id: int, query_param: str = None):
    return {"item_id": item_id, "query_param": query_param}

官方文档: https://fastapi.tiangolo.com/

30、数据库连接 - SQLAlchemy

from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.orm import sessionmaker
from sqlalchemy.ext.declarative import declarative_base

engine = create_engine('sqlite:///mydatabase.db')
Base = declarative_base()

class User(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    name = Column(String)

Session = sessionmaker(bind=engine)
session = Session()

官方文档: https://docs.sqlalchemy.org/en/20/

31、文本处理 - NLTK

import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize

text = "This is a sample sentence."
tokens = word_tokenize(text)
print(tokens)

官方文档: https://www.nltk.org/

32、命令行应用 - argparse

import argparse

parser = argparse.ArgumentParser(description='A simple command-line app')
parser.add_argument('--name', type=str, help='Your name')
args = parser.parse_args()
print(f'Hello, {args.name}!')

官方文档: https://docs.python.org/3/library/argparse.html

33、微服务 - Flask-RESTful

from flask import Flask
from flask_restful import Resource, Api

app = Flask(__name)
api = Api(app)

class HelloWorld(Resource):
    def get(self):
        return {'message': 'Hello, World!'}

api.add_resource(HelloWorld, '/')

官方文档: https://flask-restful.readthedocs.io/en/latest/

34、数据处理 - BeautifulSoup

from bs4 import BeautifulSoup
import requests

url = "https://www.example.com"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
print(soup.title.text)

官方文档: https://www.crummy.com/software/BeautifulSoup/bs4/doc/

35、加密 - hashlib

import hashlib

text = "Secret Password"
hash_object = hashlib.sha256(text.encode())
hash_hex = hash_object.hexdigest()
print(hash_hex)

官方文档: https://docs.python.org/3/library/hashlib.html

36、数据序列化 - Pickle

import pickle

data = {'name': 'Alice', 'age': 30}
with open('data.pkl', 'wb') as file:
    pickle.dump(data, file)

with open('data.pkl', 'rb') as file:
    loaded_data = pickle.load(file)
    print(loaded_data)

官方文档: https://docs.python.org/3/library/pickle.html

37、并行处理 - concurrent.futures

import concurrent.futures

def square(x):
    return x * x

with concurrent.futures.ThreadPoolExecutor() as executor:
    results = executor.map(square, [1, 2, 3, 4, 5])

for result in results:
    print(result)

官方文档: https://docs.python.org/3/library/concurrent.futures.html

38、网络爬虫 - Scrapy

import scrapy

class MySpider(scrapy.Spider):
    name = 'example.com'
    start_urls = ['https://www.example.com']

    def parse(self, response):
        # 爬取和处理数据
        pass

官方文档: https://docs.scrapy.org/en/latest/

39、异步编程 - asyncio

import asyncio

async def hello():
    await asyncio.sleep(1)
    print("Hello, Async!")

loop = asyncio.get_event_loop()
loop.run_until_complete(hello())

官方文档: https://docs.python.org/3/library/asyncio.html

40、数据分析 - Numpy

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
print(arr.mean())

官方文档: https://numpy.org/doc/stable/

41、数据处理 - Pandas

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)
print(df)

官方文档: https://pandas.pydata.org/docs/

42、数据可视化 - Matplotlib

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 15, 13, 18, 20]
plt.plot(x, y)
plt.show()

官方文档: https://matplotlib.org/stable/contents.html

43、机器学习 - Scikit-Learn

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2)
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)

官方文档: https://scikit-learn.org/stable/documentation.html

44、机器学习 - Keras

from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))

官方文档: https://keras.io/guides/

45、图像处理 - OpenCV

import cv2

image = cv2.imread('image.jpg')
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

官方文档: https://docs.opencv.org/master/index.html

46、数据爬取 - Scrapy

import scrapy

class MySpider(scrapy.Spider):
    name = 'example.com'
    start_urls = ['https://www.example.com']

    def parse(self, response):
        # 爬取和处理数据
        pass

47、数据分析 - Seaborn

import seaborn as sns
import matplotlib.pyplot as plt

data = sns.load_dataset("iris")
sns.pairplot(data, hue="species")
plt.show()

官方文档: https://seaborn.pydata.org/introduction.html

48、数据可视化 - Plotly

import plotly.express as px

fig = px.scatter(x=[1, 2, 3, 4], y=[10, 11, 12, 13])
fig.show()

官方文档: https://plotly.com/python/

49、自然语言处理 - spaCy

import spacy

nlp = spacy.load('en_core_web_sm')
doc = nlp("This is a sample sentence.")
for token in doc:
    print(token.text, token.pos_)

官方文档: https://spacy.io/usage/spacy-101

50、机器学习 - XGBoost

import xgboost as xgb

data = xgb.DMatrix('train.csv')
param = {'max_depth': 3, 'eta': 0.1, 'objective': 'reg:squarederror'}
model = xgb.train(param, data, 10)

官方文档: https://xgboost.readthedocs.io/en/latest/

结束 over