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()