解锁 Python 超能力：十大变革性库与框架

import numpy as np

# 创建一个简单的数组
arr = np.array([1, 2, 3])
print("简单数组:", arr)

# 数组的数学运算
arr_squared = arr ** 2
print("平方后的数组:", arr_squared)

# 利用数组进行快速计算
sum_arr = np.sum(arr)
mean_arr = np.mean(arr)
print(f"总和: {sum_arr}, 平均值: {mean_arr}")
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import pandas as pd

# 创建一个简单的DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [24, 30, 19]}
df = pd.DataFrame(data)
print("简单的DataFrame:")
print(df)

# 数据清洗：删除空值，添加新列
df['Age'] = df['Age'].fillna(0)  # 假设有个别空值
df['City'] = ['NY', 'LA', 'SF']  # 新增一列
print("\n处理后的DataFrame:")
print(df)

# 简单统计分析
print("\n年龄的平均值:", df['Age'].mean())
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import matplotlib.pyplot as plt

# 准备数据
x = [1, 2, 3, 4]
y = [10, 15, 7, 10]

# 创建图表
plt.plot(x, y, marker='o')
plt.title('Simple Line Chart')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()
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import seaborn as sns
import pandas as pd

# 假设有一个数据集
data = pd.DataFrame([[1, 2], [2, 3]], columns=['A', 'B'], index=['X', 'Y'])
sns.heatmap(data, annot=True, cmap='coolwarm')
plt.show()
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import requests

url = 'https://api.example.com/data'
response = requests.get(url)

if response.status_code == 200:
    print("成功获取数据:", response.text)
else:
    print("请求失败，状态码:", response.status_code)
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from bs4 import BeautifulSoup
import requests

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

title = soup.find('title').text
print("网页标题:", title)
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from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston

# 加载波士顿房价数据集
boston = load_boston()
X, y = boston.data, boston.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建并训练模型
model = LinearRegression()
model.fit(X_train, y_train)

# 预测
predictions = model.predict(X_test)

# 评估
print("模型得分:", model.score(X_test, y_test))
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import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.datasets import mnist

# 加载数据
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# 构建模型
model = Sequential([
    Flatten(input_shape=(28, 28)),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])

# 编译模型
model.compile(optimizer='adam', 
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 训练模型
model.fit(x_train, y_train, epochs=5)

# 评估模型
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print('\nTest accuracy:', test_acc)
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from flask import Flask

app = Flask(__name__)

@app.route('/')
def hello_world():
    return 'Hello, World!'

if __name__ == '__main__':
    app.run(debug=True)
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django-admin startproject myproject
cd myproject
python manage.py startapp myapp
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