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Python是一種功能強(qiáng)大的編程語(yǔ)言，被廣泛應(yīng)用于數(shù)據(jù)分析、人工智能、網(wǎng)站開(kāi)發(fā)等各個(gè)領(lǐng)域。它具有簡(jiǎn)潔易讀的語(yǔ)法和豐富的庫(kù)，讓編程變得簡(jiǎn)單高效。若想更好地掌握Python，開(kāi)啟自己的創(chuàng)新之旅，不妨嘗試一些具體的代碼示例。

數(shù)據(jù)分析與可視化

import pandas as pd
import matplotlib.pyplot as plt

data = {'Name': ['Alice', 'Bob', 'Cathy', 'David'],
        'Age': [25, 30, 35, 40]}
df = pd.DataFrame(data)

plt.bar(df['Name'], df['Age'])
plt.xlabel('Name')
plt.ylabel('Age')
plt.title('Age Distribution')
plt.show()

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簡(jiǎn)單的爬蟲(chóng)

import requests
from bs4 import BeautifulSoup

url = 'http://example.com'
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
print(soup.prettify())

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機(jī)器學(xué)習(xí)實(shí)踐

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

iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = RandomForestClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)

print('Accuracy:', accuracy_score(y_test, predictions))

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通過(guò)這些簡(jiǎn)單的示例，你可以體驗(yàn)Python在數(shù)據(jù)分析、網(wǎng)絡(luò)爬