我正在尝试使用循环将多个分散字形绘制到一个图形上。目标是为每个品牌使用自己的标志符号,并在“选择”小部件选择了另一个值的情况下更新值。
但是该图显示了一个空图。我认为问题是ColumnDataSource和函数“ update()”中的更新。这是一个可执行的示例。你有什么主意吗?
from bokeh.layouts import column, row
from bokeh.models import ColumnDataSource, Select, HoverTool
from bokeh.plotting import figure, show
import pandas as pd
brands = ['a', 'b', 'c', 'a', 'b', 'b', 'c']
product = ['v1', 'v2', 'v3', 'v4', 'v5', 'v6', 'v7']
price = [2, 3, 54, 48, 9, 2, 4]
size = [10, 11, 12, 13, 14, 15, 16]
value = [5, 4, 3, 8, 1, 0, 1]
id = [1, 2, 3, 4, 5, 6, 7]
col = ['ID', 'brand', 'product', 'price', 'size', 'value']
label = ['price', 'size', 'value']
df = pd.DataFrame(zip(brands, product, price, size, value, id), columns=col)
# Widgets:
select_x_axis = Select(title="x-Axis:", value=label[0], options=label)
select_y_axis = Select(title="y-Axis:", value=label[1], options=label)
# Set up figure
hover = HoverTool(tooltips=[
("index", "@id"),
('Brand', '@brand'),
('Product', '@product'),
(select_x_axis.value, '@x'),
(select_y_axis.value, '@y')
])
# Set up plots:
fig = figure(plot_height=400, plot_width=800, title="xyz",
# tooltips=TOOLTIPS,
tools=[hover, 'reset'],
x_axis_label=select_x_axis.value,
y_axis_label=select_y_axis.value)
source = {}
plots = {}
for brand in brands:
# Create Column Data Source that will be used by the plot
source[brand] = ColumnDataSource(data=dict(x=[], y=[], id=[], product=[], brand=[]))
plots[brand] = fig.scatter(x='x', y='y', size=5, source=source[brand])
def update():
x_name = select_x_axis.value
y_name = select_y_axis.value
fig.xaxis.axis_label = x_name
fig.yaxis.axis_label = y_name
for brand in brands:
df1 = df.loc[df['brand'] == brand]
source[brand].data = dict(
x=df1[x_name],
y=df1[y_name],
id=df1['ID'],
product=df1['product'],
brand=df1['brand']
)
# Set up layouts and add to document
controls = [select_x_axis, select_y_axis]
for control in controls:
control.on_change('value', lambda attr, old, new: update())
inputs = column(select_x_axis, select_y_axis)
update() # initial load of the data
show(row(inputs, fig, width=1000))
#curdoc().add_root(row(inputs, fig, width=1000))
#curdoc().title = "xyz"
类似的东西?
from bokeh.layouts import column, row
from bokeh.models import ColumnDataSource, Select, HoverTool, CustomJS
from bokeh.plotting import figure, show
import pandas as pd
def make_data() :
brands = ['a', 'b', 'c', 'a', 'b', 'b', 'c']
product = ['v1', 'v2', 'v3', 'v4', 'v5', 'v6', 'v7']
price = [2, 3, 54, 48, 9, 2, 4]
size = [10, 11, 12, 13, 14, 15, 16]
value = [5, 4, 3, 8, 1, 0, 1]
ident = [1, 2, 3, 4, 5, 6, 7]
col = ['ID', 'brand', 'product', 'price', 'size', 'value']
label = ['price', 'size', 'value']
return pd.DataFrame(zip(brands, product, price, size, value, ident), columns=col)
if __name__ == "__main__" :
df = make_data()
default_xcol = "price"
default_ycol = "size"
df["xvalues"] = df[default_xcol]
df["yvalues"] = df[default_ycol]
# Scatter plot
hover = HoverTool(tooltips=[(name, "@" + name) for name in df.columns])
figure = figure(tools=[hover, 'reset'])
# Source
source = ColumnDataSource(df)
figure.scatter("xvalues", "yvalues", source=source)
# Selects
options = ["product", "price", "size", "value"]
select_x_axis = Select(title="x-Axis:", value=default_xcol, options=options)
select_y_axis = Select(title="y-Axis:", value=default_ycol, options=options)
# callback
callback = CustomJS(args={"source":source, "axis":figure.xaxis[0]}, code="""
source.data['xvalues'] = source.data[cb_obj.value];
source.change.emit();
axis.axis_label = cb_obj.value;
""")
select_x_axis.js_on_change("value", callback)
callback = CustomJS(args={"source":source, "axis":figure.yaxis[0]}, code="""
source.data['yvalues'] = source.data[cb_obj.value];
source.change.emit();
axis.axis_label = cb_obj.value;
""")
select_y_axis.js_on_change("value", callback)
show(row(
column(select_x_axis, select_y_axis),
figure, width=1000))