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- import pandas as pd
- import plotly.graph_objects as go
- from plotly.subplots import make_subplots
-
- # 假设你的DataFrame叫做df,并且已经包含了所需字段
- # 如果你的数据是CSV文件,可以使用pd.read_csv('your_file.csv')来加载数据
- # df = pd.read_csv('your_file.csv')
-
- # 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)
-
- # 创建颜色映射,将每个年月映射到一个唯一的颜色
- unique_months = df['年月'].unique()
- colors = [f'rgb({i}, {150 - i}, 50)' for i in range(len(unique_months))]
- color_map = dict(zip(unique_months, colors))
-
- # 使用make_subplots创建3D散点图
- fig = make_subplots(rows=1, cols=1, specs=[[{"type": "scatter3d"}]])
-
- # 遍历DataFrame的每一行,为每个点添加数据
- for index, row in df.iterrows():
- x = row['风速']
- y = row['年月']
- z = row['有功功率']
- color = color_map[y]
-
- # 添加散点到子图
- fig.add_trace(go.Scatter3d(x=[x], y=[y], z=[z], mode='markers', marker=dict(color=color)), row=1, col=1)
-
- # 更新子图的布局,设置y轴为category类型,并设置其类别顺序
- fig.update_layout(
- title='3D散点图:风速、年月与有功功率',
- margin=dict(l=0, r=0, b=0, t=0),
- scene=dict(
- xaxis=dict(title='风速'),
- yaxis=dict(title='年月', tickmode='array', tickvals=unique_months, ticktext=unique_months, categoryorder='category ascending'),
- zaxis=dict(title='有功功率')
- )
- )
-
- # 显示图形
- fig.show()
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