结合 NumPy 和 Matplotlib 进行数据可视化的十种创意

import numpy as np
import matplotlib.pyplot as plt

# 创建一个简单的数组
x = np.linspace(0, 10, 100)
y = np.sin(x)

# 使用 Matplotlib 绘制图形
plt.plot(x, y)
plt.title('Sine Wave')
plt.xlabel('Time')
plt.ylabel('Amplitude')
plt.show()

# 创建两个不同的数据系列
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# 绘制两个数据系列
plt.plot(x, y1, label='sin(x)')
plt.plot(x, y2, label='cos(x)')
plt.legend()  # 显示图例
plt.show()

# 创建随机数据
x = np.random.randn(100)
y = np.random.randn(100)

# 绘制散点图
plt.scatter(x, y)
plt.title('Scatter Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

# 创建随机数据
data = np.random.randn(1000)

# 绘制直方图
plt.hist(data, bins=30, alpha=0.7)
plt.title('Histogram')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()

# 创建网格数据
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sqrt(X**2 + Y**2)

# 绘制等高线图
plt.contourf(X, Y, Z, 20, cmap='viridis')
plt.colorbar()
plt.title('Contour Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

# 创建一个随机的二维数据矩阵
data = np.random.rand(10, 10)

# 绘制热力图
plt.imshow(data, cmap='hot', interpolation='nearest')
plt.colorbar()
plt.title('Heatmap')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

# 创建分类数据
labels = ['A', 'B', 'C', 'D']
sizes = [15, 30, 45, 10]

# 绘制饼图
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)
plt.title('Pie Chart')
plt.show()

# 创建随机数据
data = np.random.randn(100)

# 绘制箱线图
plt.boxplot(data)
plt.title('Box Plot')
plt.ylabel('Value')
plt.show()

from mpl_toolkits.mplot3d import Axes3D

# 创建三维数据
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# 创建三维图形
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap='viridis')

ax.set_title('3D Surface Plot')
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.set_zlabel('Z-axis')
plt.show()

import matplotlib.animation as animation

# 创建数据
x = np.linspace(0, 10, 100)
y = np.sin(x)

# 创建图形对象
fig, ax = plt.subplots()
line, = ax.plot(x, y)

# 更新函数
def update(frame):
    line.set_ydata(np.sin(x + frame / 10.0))
    return line,

# 创建动画
ani = animation.FuncAnimation(fig, update, frames=100, interval=50)
plt.title('Dynamic Sine Wave')
plt.xlabel('Time')
plt.ylabel('Amplitude')
plt.show()

import pandas as pd
import yfinance as yf

# 下载股票数据
ticker = 'AAPL'
data = yf.download(ticker, start='2022-01-01', end='2023-01-01')

# 绘制股票价格走势图
plt.figure(figsize=(12, 6))
plt.plot(data['Close'], label='Close Price')
plt.title(f'{ticker} Stock Price')
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.legend()
plt.show()

# 计算移动平均线
data['MA50'] = data['Close'].rolling(window=50).mean()
data['MA200'] = data['Close'].rolling(window=200).mean()

# 绘制移动平均线
plt.figure(figsize=(12, 6))
plt.plot(data['Close'], label='Close Price')
plt.plot(data['MA50'], label='50-Day MA')
plt.plot(data['MA200'], label='200-Day MA')
plt.title(f'{ticker} Stock Price with Moving Averages')
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.legend()
plt.show()