Python-Plotly库实现数据动态可视化的示例代码

pip install plotly

import plotly.graph_objects as go
import numpy as np

x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)

fig = go.Figure(data=go.Scatter(x=x, y=y, mode='lines'))
fig.update_layout(title='正弦函数', xaxis_title='x', yaxis_title='y')
fig.show()

import plotly.graph_objects as go
import numpy as np

y = np.linspace(-10, 10, 100)
x= y**2

fig = go.Figure(data=go.Scatter(x=x, y=y, mode='lines'))
fig.update_traces(line=dict(color='red'))
fig.update_layout(title='抛物线函数', xaxis_title='x', yaxis_title='y')
fig.show()

import plotly.graph_objects as go
import numpy as np

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))

fig = go.Figure(data=go.Surface(z=z, x=x, y=y))

fig.update_layout(title='三维曲面', scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'))
fig.show()

import plotly.graph_objects as go
import numpy as np
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-2, 2, 100)
x = z * np.sin(theta)
y = z * np.cos(theta)

fig = go.Figure(data=go.Scatter3d(x=x, y=y, z=z, mode='lines'))
fig.update_traces(line=dict(color='orange', width=2))
fig.update_layout(title='三维曲线', scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'))
fig.show()

import plotly.graph_objects as go
import numpy as np

u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, np.pi, 100)
x = np.outer(np.cos(u), np.sin(v))
y = np.outer(np.sin(u), np.sin(v))
z = np.outer(np.ones(np.size(u)), np.cos(v))

fig = go.Figure(data=go.Surface(z=z, x=x, y=y, colorscale='Reds'))
fig.update_layout(title='球面', scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'))
fig.show()

import plotly.graph_objects as go
import numpy as np

u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(-0.5, 0.5, 100)
u, v = np.meshgrid(u, v)
x = (1 + v * np.cos(u / 2)) * np.cos(u)
y = (1 + v * np.cos(u / 2)) * np.sin(u)
z = v * np.sin(u / 2)

fig = go.Figure(data=go.Surface(z=z, x=x, y=y, colorscale='rainbow'))
fig.update_layout(title='莫比乌斯带', scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'))
fig.show()

import plotly.graph_objects as go

fig = go.Figure()

# 添加多条折线
fig.add_trace(go.Scatter(y=[2, 1, 3], name="Line 1"))
fig.add_trace(go.Scatter(y=[3, 2, 1], name="Line 2"))

# 配置交互按钮
fig.update_layout(
    updatemenus=[
        dict(
            type="buttons",
            direction="right",
            buttons=[
                dict(label="Show All", method="update", args=[{"visible": [True, True]}]),
                dict(label="Show Line 1", method="update", args=[{"visible": [True, False]}]),
                dict(label="Show Line 2", method="update", args=[{"visible": [False, True]}]),
            ],
        )
    ]
)

fig.show()

import plotly.express as px
import numpy as np

# 模拟大数据
np.random.seed(42)
x = np.random.rand(100_000)
y = np.random.rand(100_000)

# 使用WebGL加速的散点图
fig = px.scatter(x=x, y=y, render_mode='webgl', title="WebGL Accelerated Scatter Plot")
fig.show()

from dash import Dash, dcc, html, Input, Output
import plotly.graph_objects as go
import pandas as pd

# 模拟疫情数据
data = {
    "date": pd.date_range(start="2020-01-01", periods=100),
    "cases": [i ** 2 for i in range(100)],
    "deaths": [i * 5 for i in range(100)]
}
df = pd.DataFrame(data)

app = Dash(__name__)

app.layout = html.Div([
    dcc.Graph(id="graph"),
    dcc.Slider(
        id="year-slider",
        min=0,
        max=len(df) - 1,
        value=0,
        marks={i: str(df["date"][i].date()) for i in range(0, len(df), 10)},
        step=None
    )
])


@app.callback(Output("graph", "figure"), [Input("year-slider", "value")])
def update_graph(selected_index):
    filtered_df = df.iloc[:selected_index + 1]

    # 使用go.Scatter同时显示折线和数据点
    fig = go.Figure()
    for col in ["cases", "deaths"]:
        fig.add_trace(go.Scatter(
            x=filtered_df["date"],
            y=filtered_df[col],
            mode='lines+markers',
            marker=dict(size=8, color='red'),
            name=col
        ))

    fig.update_layout(title="Covid-19 Cases Over Time", xaxis_title="date", yaxis_title="value")
    return fig


if __name__ == "__main__":
    app.run(debug=True)