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- import pandas as pd
- import plotly.graph_objects as go
-
- # 示例数据
- data = {
- '机组名': ['机组A', '机组B', '机组C', '机组D'],
- '时间': ['2024-01-09 09:13:29', '2024-01-10 10:14:30', '2024-02-09 08:13:29', '2024-02-10 09:14:30'],
- '年月': ['2024-01', '2024-01', '2024-02', '2024-02'],
- '风速': [5.0, 6.0, 4.5, 5.5],
- '有功功率': [1000, 1200, 900, 1100]
- }
-
- df = pd.DataFrame(data)
-
- # 按风速升序排列数据
- df_sorted = df.sort_values(by='风速')
-
- # 获取唯一年月
- unique_months = df_sorted['年月'].unique()
-
- # 自定义颜色列表(确保颜色数量与唯一月份的数量相匹配)
- colors = ['red', 'blue', 'green', 'purple'] # 根据实际唯一月份数量调整颜色数量
-
- # 创建颜色映射
- color_map = dict(zip(unique_months, colors))
-
- # 使用go.Scatter3d创建3D散点图
- trace = go.Scatter3d(
- x=df_sorted['风速'],
- y=df_sorted['有功功率'],
- z=[color_map[month] for month in df_sorted['年月']],
- mode='markers',
- marker=dict(
- color=[color_map[month] for month in df_sorted['年月']],
- size=10,
- line=dict(color='rgba(255, 255, 255, 0.8)', width=0.5),
- opacity=0.8
- )
- )
-
- # 创建图形
- fig = go.Figure(data=[trace])
-
- # 更新图形的布局
- fig.update_layout(
- title='按风速升序排列的3D散点图:风速、有功功率与年月',
- margin=dict(l=0, r=0, b=0, t=0),
- scene=dict(
- xaxis=dict(title='风速'),
- yaxis=dict(title='有功功率'),
- zaxis=dict(
- title='年月',
- tickmode='array',
- tickvals=unique_months,
- ticktext=unique_months,
- categoryorder='category ascending'
- )
- )
- )
-
- # 显示图形
- fig.show()
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