Question

我正在尝试在POC模型中工作，在该模型中我可以添加一些交互性（例如根据vbar图将更改的方式来选择学生）。我正在使用带有标记的基本学生数据。

数据如下：

Column 1
Name
Ayan
Deepa
Sayan
Shobhit

Column 2
Marks
98
96
92
94

使用以下代码，我可以实现什么：

我能够创建函数并能够在Bokeh Server输出中获取输出。我还可以创建一个on_change回调，该回调根据下拉选择中的用户输入重新创建数据集。

需要帮助的地方：

我无法更新自己的地块中的源。我从各种在线站点尝试了各种方法，但是无法这样做。

面临的一些问题是：

当我使用数据框创建ColumnDataSource时，输出图将变为空白
如果我使用数据框而不是ColumnDataSource，则更新功能显示它无法更改df或列表

代码：

## Packages

from bokeh.core.properties import value
from bokeh.io import curdoc
from bokeh.plotting import figure
from bokeh.layouts import layout, column, gridplot, Row, widgetbox, row
from bokeh.models import TapTool, HoverTool, ColumnDataSource, Range1d, BoxSelectTool, LinearAxis, Range1d
from bokeh.models.widgets import Button, RadioButtonGroup, Select, Slider, CheckboxGroup, Panel, Tabs
from bokeh.models.annotations import LabelSet


_tools_to_show = 'box_zoom,pan,save,hover,resize,reset,tap,wheel_zoom'  




import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import datetime

## Creating Dataset

def make_dataset(Input):

    global Piv_INCS2_CD_Plot3old
    global Piv_INCS2_CD_Plot3
    global Week_List
    global P2_Major
    global Total_Incidents_Created
    global Resolution_SLO_Miss_Percent
    global new_src
    global Piv_INCS2_CD_Plot3_List
    global Old
    global Old_Filter
    global Old_CDS
    global Name
    global Marks
    global Names2
    global Old_CDS_Name
    global Old_CDS_Marks

    print("select 2 =", select.value )
    print("Input 2 =", Input)

    Old = pd.read_csv('Check_Data.csv', encoding='ISO-8859-1')
    Old_Filter = pd.DataFrame(Old[Old.Name == Input])
    Old_Filter.to_csv('Old_Filter.csv')


    Name = [Input]
    print("Name = ", Name)
    Names2 = Old_Filter["Name"].tolist()
    Marks = Old_Filter["Marks"].tolist()
    print("Names = ", Name )
    print("Marks = ", Marks )



    Old_CDS = ColumnDataSource(data = Old_Filter)
    print("OLD_CDS = ", Old_CDS)

    Old_CDS_Name = ColumnDataSource(data = {'Name':Name})
    Old_CDS_Marks = ColumnDataSource(data = {'Marks': Marks})


    return Old_CDS



##  Creating Plot


def plot(Old_CDS):


    global p3

    p3 = figure(plot_height=630, plot_width=1000, title="Marks Trend",
                   toolbar_location=None, tools="")
    p3.vbar(x = "Name", top = "Marks", width = 0.9, source=Old_CDS)

    p3.xgrid.grid_line_color = None
    p3.y_range.start = 0


    return p3 # returns the plot

## On Change Function

def update(attr, old, new):

    global Piv_INCS2_CD_Plot3_New
    global Week_List_New
    global Old_CDS_1
    global p3
    global lay
    global Old_CDS_Name_2


    Old_CDS_1 = make_dataset(select.value)
    Old_CDS.data.update(Old_CDS_1.data)

## Selection Option   

options=[("Ayan","Ayan"),("Deepa","Deepa")]
select=Select(title="Name",options=options)
print("select=", select.value )

## Changing value based on user input

select.on_change("value",update)

## Defining intial user selection

Initial_Input = "Ayan"

Old_CDS_2 = make_dataset(Initial_Input)

## Defining Layout

p3 = plot(Old_CDS_2)
lay = row(p3, select)

curdoc().add_root(lay)

预期结果：我应该能够在页面中查看vbar图表，并且当我从下拉列表中更改用户时，绘图也会发生变化

Answer 1

这是您的工作代码（适用于Bokeh v1.0.4）。更改：

x_range = Old_CDS.data['Name']添加了

global Old_CDS_2

Old_CDS_2.data['Marks'] = Old_CDS_1.data['Marks']您正在更新Old_CDS，而不是传递给Old_CDS_2

的vbars

from bokeh.core.properties import value
from bokeh.io import curdoc
from bokeh.plotting import figure
from bokeh.layouts import layout, column, gridplot, Row, widgetbox, row
from bokeh.models import TapTool, HoverTool, ColumnDataSource, Range1d, BoxSelectTool, LinearAxis, Range1d
from bokeh.models.widgets import Button, RadioButtonGroup, Select, Slider, CheckboxGroup, Panel, Tabs
from bokeh.models.annotations import LabelSet

_tools_to_show = 'box_zoom,pan,save,hover,resize,reset,tap,wheel_zoom'

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import datetime
import os

# # Creating Dataset
def make_dataset(Input):

    global Piv_INCS2_CD_Plot3old
    global Piv_INCS2_CD_Plot3
    global Week_List
    global P2_Major
    global Total_Incidents_Created
    global Resolution_SLO_Miss_Percent
    global new_src
    global Piv_INCS2_CD_Plot3_List
    global Old
    global Old_Filter
    global Old_CDS
    global Name
    global Marks
    global Names2
    global Old_CDS_Name
    global Old_CDS_Marks

    print("select 2 =", select.value)
    print("Input 2 =", Input)

    Old = pd.read_csv(os.path.join(os.path.dirname(__file__), 'Check_Data.csv'), encoding = 'ISO-8859-1')
    Old_Filter = pd.DataFrame(Old[Old.Name == Input])
    Old_Filter.to_csv(os.path.join(os.path.dirname(__file__), 'Old_Filter.csv'), index = False)

    select.options = [(name, name) for name in Old['Name'].values]
    print options

    Name = [Input]
    print("Name = ", Name)
    Names2 = Old_Filter["Name"].tolist()

    Marks = Old_Filter["Marks"].tolist()
    print("Names = ", Name)
    print("Marks = ", Marks)

    Old_CDS = ColumnDataSource(data = Old_Filter)
    print("OLD_CDS = ", Old_CDS)

    Old_CDS_Name = ColumnDataSource(data = {'Name': Name})
    Old_CDS_Marks = ColumnDataSource(data = {'Marks': Marks})

    return Old_CDS

# #  Creating Plot
def plot(Old_CDS):

    global p3

    print Old_CDS.data
    p3 = figure(plot_height = 630, plot_width = 1000, title = "Marks Trend", x_range = Old_CDS.data['Name'],
                   toolbar_location = None, tools = "")
    p3.vbar(x = "Name", top = "Marks", width = 0.9, source = Old_CDS)

    p3.xgrid.grid_line_color = None
    p3.y_range.start = 0

    return p3  # returns the plot

# # On Change Function
def update(attr, old, new):

    global Piv_INCS2_CD_Plot3_New
    global Week_List_New
    global Old_CDS_1
    global p3
    global lay
    global Old_CDS_Name_2
    global Old_CDS_2

    Old_CDS_1 = make_dataset(select.value)
    Old_CDS_2.data['Marks'] = Old_CDS_1.data['Marks']

# # Selection Option
options = [("Ayan", "Ayan"), ("Deepa", "Deepa")]
select = Select(title = "Name", options = options)
print("select=", select.value)

# # Changing value based on user input
select.on_change("value", update)

# # Defining intial user selection
Initial_Input = "Ayan"

Old_CDS_2 = make_dataset(Initial_Input)

# # Defining Layout
p3 = plot(Old_CDS_2)
lay = row(p3, select)
curdoc().add_root(lay)

结果：

如何使用Python使bokeh vbar交互？

1 个答案: