Python Dynamic Charts Creation Guide

In Python, various libraries can be used to create interactive data plots, with the most popular ones being Matplotlib and Plotly. Here is a simple example of creating dynamic data plots using these two libraries.

Create dynamic data plots using Matplotlib.

import matplotlib.pyplot as plt
import numpy as np

# 创建动态图的数据
x = np.linspace(0, 10, 100)
y = np.sin(x)

# 创建图像和轴对象
fig, ax = plt.subplots()
line, = ax.plot(x, y)

# 更新数据函数
def update(i):
    line.set_ydata(np.sin(x + i/10))
    return line,

# 创建动画
ani = FuncAnimation(fig, update, frames=np.arange(0, 10, 0.1), interval=200)

# 显示动画
plt.show()

Create dynamic data plots using Plotly.

import plotly.graph_objects as go
import numpy as np

# 创建动态图的数据
x = np.linspace(0, 10, 100)
y = np.sin(x)

# 创建图像和追踪对象
fig = go.Figure(data=go.Scatter(x=x, y=y))
fig.update_layout(title="Dynamic Data", xaxis_title="x", yaxis_title="y")

# 更新数据函数
def update(i):
    fig.data[0].y = np.sin(x + i/10)

# 创建动画
fig.frames = [go.Frame(data=go.Scatter(x=x, y=np.sin(x + i/10))) for i in np.arange(0, 10, 0.1)]
fig.layout.updatemenus = [dict(type="buttons", buttons=[dict(label="Play", method="animate",
                                                             args=[None, {"frame": {"duration": 200, "redraw": False},
                                                                           "fromcurrent": True, "transition": {"duration": 0}}])])]
# 显示动画
fig.show()

These examples demonstrate how to create dynamic data graphs using Matplotlib and Plotly. You can customize and expand upon these examples based on your own needs.