子圖

使用 Bokeh 製作互動式資料視覺化

George Boorman

Core Curriculum Manager, DataCamp

為何使用子圖？

直方圖

高 vs 低列子圖

建立橫列

from bokeh.layouts import row

east_source = ColumnDataSource(data=east)
west_source = ColumnDataSource(data=west)
fig_one = figure(x_axis_label="Assists per Game", y_axis_label="Points per Game")
fig_two = figure(x_axis_label="Assists per Game", y_axis_label="Points per Game")

fig_one.circle(x="assists", y="points", source=east_source, color="blue", legend_label="East")
fig_two.circle(x="assists", y="points", source=west_source, color="blue", legend_label="West")

output_file(filename="row_plots.html")

show(row(fig_one, fig_two))

列子圖

NBA 列子圖

欄子圖

from bokeh.layouts import column

fig_one = figure(x_axis_label="Assists", 
                 y_axis_label="Rebounds")
fig_two = figure(x_axis_label="Assists", 
                 y_axis_label="Rebounds")
fig_one.circle(x="assists", y="rebounds", 
               source=east_source, 
               color="blue", legend_label="East")
fig_two.circle(x="assists", y="rebounds", 
               source=east_source, 
               color="blue", legend_label="East")

output_file(filename="column_plots.html")
show(column(fig_one, fig_two))

基本直欄圖

建立網格圖

from bokeh.layouts import gridplot

positions = ["PG", "SG", "SF", "PF"]

plots = []

for position in positions:

  nba_positions = nba.loc[nba["position"] == position]
  source = ColumnDataSource(data=nba_positions)

  fig = figure(x_axis_label="Assists", y_axis_label="Points")

  fig.circle(x="assists", y="points", source=source, legend_label=position)

  plots.append(fig)

output_file(filename="nba_gridplot.html")
show(gridplot(plots, ncols=2))

網格圖

自訂圖形尺寸

fig = figure(x_axis_label="Assists", y_axis_label="Rebounds",

             width=750, height=300)

fig.circle(x="assists", y="rebounds", source=source)
output_file(filename="custom_size_plot.html")
show(fig)

自訂尺寸圖

一起來練習吧！

使用 Bokeh 製作互動式資料視覺化