scatter(df, x="date", y="GOOG", trendline="lowess", trendline_options=dict(frac=0.1)) fig.show() 线图 px.line()绘制线图 #简单折图 import plotly.express as px fig = px.line(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16]) fig.show() #带误差线的折现图 import plotly.express...
help_fig = px.scatter(df, x=df['dates'], y=df['count'] , trendline="lowess") # extract points as plain x and y x_trend = help_fig["data"][1]['x'] y_trend = help_fig["data"][1]['y'] # add the x,y data as a scatter graph object fig.add_trace( go.Scatter(x=x_...
help_fig = px.scatter(df, x=df['dates'], y=df['count'] , trendline="lowess") # extract points as plain x and y x_trend = help_fig["data"][1]['x'] y_trend = help_fig["data"][1]['y'] # add the x,y data as a scatter graph object fig.add_trace( go.Scatter(x=x_...
help_fig = px.scatter(df, x=df['dates'], y=df['count'] , trendline="lowess") # extract points as plain x and y x_trend = help_fig["data"][1]['x'] y_trend = help_fig["data"][1]['y'] # add the x,y data as a scatter graph object fig.add_trace( go.Scatter(x=x_...
利用Pandas Groupby()、for loops和Plotly Scatter Graph对象结合Plotly Express趋势线创建带有回归趋势线的时间序列图。 数据 为了说明这是如何工作的,让我们假设我们有一个简单的数据集,它有一个datetime列和几个其他分类列。您感兴趣的是某一列(“类型”)在一段时间内(“日期”)的汇总计数。列可以是数字、类别或...
plotlyplotly_whiteplotly_darkpx.scatter( iris, # 数据集 x="sepal_width", # 横坐标值 y="sepal_length", # 纵坐标取值 color="species", # 颜色 marginal_x="box", # 横坐标为箱型图 marginal_y="histogram", # 纵坐标为直方图 height=600, # 高度 trendline="ols", # 显示趋势线 template="...
scatter-散点图 In a scatter plot, each row ofdata_frameis represented by a symbol mark in 2D space. px.scatter(data_frame=None,x=None,y=None,color=None,symbol=None,size=None,hover_name=None,hover_data=None,custom_data=None,text=None,facet_row=None,facet_col=None,facet_col_wrap=0,...
与seaborn类似,plotly图表主题不需要单独设置,使用默认参数即可满足正常情况下的使用,因此一行代码并设置参数trendline="ols"即可搞定散点图与拟合线的绘制,非常方便。 import plotly.express as px fig=px.scatter(df, x="open", y="close", trendline="ols") ...
scatter_geo-基于地图的散点图 In a geographic scatter plot, each row ofdata_frameis represented by a symbol mark on a map. 代码语言:javascript 复制 px.scatter_geo(data_frame=None,lat=None,lon=None,locations=None,locationmode=None,color=None,text=None,hover_name=None,hover_data=None,custom...
iris=px.data.iris()fig=px.scatter(iris,x="sepal_width",y="sepal_length",color="species",marginal_y="violin",marginal_x="box",trendline="ols")fig.show() 同样地,这里用的是鸢尾花的数据集。除了再次自动赋予color以外,例子中还有两个特别的参数marginal_x和marginal_y。而且传递给它们的值也很奇...