Python AI 工具链

import numpy as np

# 创建数组
arr = np.array([1, 2, 3, 4, 5])
matrix = np.array([[1, 2, 3], [4, 5, 6]])

# 数组属性
print("形状:", matrix.shape)  # (2, 3)
print("维度:", matrix.ndim)   # 2
print("数据类型:", matrix.dtype)

# 数组操作
arr2 = np.arange(0, 10, 2)  # [0, 2, 4, 6, 8]
zeros = np.zeros((3, 3))    # 3x3 零矩阵
ones = np.ones((2, 4))      # 2x4 全1矩阵

# 索引和切片
print(arr[0])       # 1
print(matrix[0, 1]) # 2
print(arr[1:4])     # [2, 3, 4]

# 数学运算（向量化）
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b)        # [5 7 9]
print(a * b)        # [4 10 18]
print(np.dot(a, b)) # 32

# 统计函数
data = np.random.randn(1000)
print("均值:", round(data.mean(), 4))
print("标准差:", round(data.std(), 4))

import pandas as pd

# 创建 DataFrame
data = {
'姓名': ['张三', '李四', '王五', '赵六'],
'年龄': [25, 30, 35, 28],
'城市': ['北京', '上海', '广州', '深圳'],
'薪资': [15000, 20000, 25000, 18000]
}
df = pd.DataFrame(data)

# 查看数据
print(df.head())
print(df.describe())

# 数据筛选
beijing = df[df['城市'] == '北京']
high_salary = df[df['薪资'] > 18000]

# 分组聚合
city_stats = df.groupby('城市')['薪资'].agg(['mean', 'max', 'min'])
print(city_stats)

# 数据处理
df['薪资等级'] = df['薪资'].apply(lambda x: '高' if x > 20000 else '中' if x > 15000 else '低')

# 读取 CSV
# df = pd.read_csv('data.csv')
# df.to_csv('output.csv', index=False)

import matplotlib.pyplot as plt
import numpy as np

# 设置中文显示
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

# 折线图
x = np.linspace(0, 10, 100)
plt.figure(figsize=(10, 6))
plt.plot(x, np.sin(x), label='sin(x)')
plt.plot(x, np.cos(x), label='cos(x)')
plt.xlabel('x')
plt.ylabel('y')
plt.title('三角函数')
plt.legend()
plt.grid(True)
plt.savefig('trig.png', dpi=300)
plt.show()

# 柱状图
categories = ['A', 'B', 'C', 'D']
values = [23, 45, 56, 78]
plt.figure(figsize=(8, 5))
plt.bar(categories, values, color=['red', 'green', 'blue', 'yellow'])
plt.title('分类统计')
plt.show()

# 散点图
np.random.seed(42)
x = np.random.randn(100)
y = 2 * x + np.random.randn(100)
plt.figure(figsize=(8, 6))
plt.scatter(x, y, alpha=0.5)
plt.xlabel('X')
plt.ylabel('Y')
plt.title('散点图')
plt.show()