用Python的Plotly画出炫酷的数据可视化(含各类图介绍)
categories=['A','B','C','D','E']values=[30,45,60,25,50]# 创建柱状图 fig=px.bar(x=categories,y=values,color=categories,title='示例柱状图')# 设置图形布局 fig.update_layout(xaxis_title='类别',yaxis_title='值',showlegend=True)# 显示图形 fig.show() 1. 2. 3. 4. 5. 6. 7....
go.Bar是Plotly中用于绘制柱状图的函数之一。 根据值添加自定义图例标签是指在柱状图中,根据每个柱子的值添加自定义的图例标签。这样可以更直观地展示每个柱子所代表的含义。 具体实现方法如下: 导入所需的库和模块: 代码语言:txt 复制 import plotly.graph_objects as go 创建柱状图的数据: 代码语言:txt 复制...
# 创建散点图 fig = go.Figure(data=go.Scatter(x=x, y=y, mode='markers', marker=dict(color='red'), name='散点数据')) # 设置图形布局 fig.update_layout( title='示例散点图', xaxis_title='X轴标签', yaxis_title='Y轴标签', showlegend=True ) # 显示图形 fig.show() 1. 2. 3....
df_Heart = df_heart[['age', 'trestbps', 'chol', 'thalach', 'oldpeak']] corr = df_Heart.corr() fig = go.Figure(data= go.Heatmap(z=corr, x=corr.index.values, y=corr.columns.values, colorscale='earth', ) ) fig.update_layout(title_text='Correlation Matrix (cont. features...
marker={"size":8,"color":"rgba(252, 108, 117, 1)",# 除此之外,还可以设置点的轮廓"line": {"width":10,# 线条大小"color":"rgba(1, 170, 118, 0.3)"# 线条的颜色} } ) fig = go.Figure(data=[trace0, trace1]) fig 现在我们又接触到了一个参数 marker,这个是给点设置样式的。结构如...
The simple_white colorbar styling has been streamlined #2110 The jupyterlab-plotly and plotlywidget JupyterLab extensions should now share code when installed together, resulting in smaller JupyterLab vendor bundle sizes #2103 Fixed Plotly Express category_orders are now respected independent of the co...
size和color参数在图中表示第三个维度。 03 3D曲面图 import plotly.graph_objects as go import numpy as np # 生成示例数据 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.add_trace(go.Bar(x=df['Date'], y=df["Flag"],name='Histogram', marker_color="green")) In [31]: # 绘制MACD曲线图 trace1 = go.Scatter(x=df['Date'], y=df['MACD'], mode='lines', name='MACD Line') trace2 = go.Scatter(x=df['Date'], y=df['Signal_Line'], mode...
color:str型,传入十六进制色彩,默认为'#444' 下面是一个简单的例子: import plotlyimport plotly.graph_objs as goimport numpy as np '''构造1000个服从二维正态分布的模拟数据'''N = 1000random_x = np.random.randn(N)random_y = np.random.randn(N) ...