为什么散景图不随图选择而改变？

Question

努力理解为什么这个散景图不允许我更改绘图并查看预测的数据。出现绘图和选择（下拉菜单）菜单，但我无法更改项目的绘图在菜单中。

通过Anaconda运行Bokeh 1.2.0。该代码已在Jupyter内部和外部运行。运行代码时无错误显示。我浏览了与该问题相关的少数SO帖子，但我无法成功应用相同的解决方案。

我不确定如何由此产生玩具问题，因此，除了下面的代码示例外，完整的代码（包括回归代码和相应的数据）可以在我的github here中找到（代码： Regression&Plotting.ipynb，数据： pred_data.csv，historical_data.csv，features_created.pkd。]

import pandas as pd
import datetime
from bokeh.io import curdoc, output_notebook, output_file
from bokeh.layouts import row, column
from bokeh.models import Select, DataRange1d, ColumnDataSource
from bokeh.plotting import figure

#Must be run from the command line
def get_historical_data(src_hist, drug_id):
    historical_data = src_hist.loc[src_hist['ndc'] == drug_id]
    historical_data.drop(['Unnamed: 0', 'date'], inplace = True, axis = 1)#.dropna()
    historical_data['date'] = pd.to_datetime(historical_data[['year', 'month', 'day']], infer_datetime_format=True)
    historical_data = historical_data.set_index(['date'])
    historical_data.sort_index(inplace = True)
    # csd_historical = ColumnDataSource(historical_data)
    return historical_data

def get_prediction_data(src_test, drug_id):
    #Assign the new date
    #Write a new dataframe with values for the new dates
    df_pred = src_test.loc[src_test['ndc'] == drug_id].copy()
    df_pred.loc[:, 'year'] = input_date.year
    df_pred.loc[:, 'month'] = input_date.month
    df_pred.loc[:, 'day'] = input_date.day
    df_pred.drop(['Unnamed: 0', 'date'], inplace = True, axis = 1)
    prediction = lin_model.predict(df_pred)
    prediction_data = pd.DataFrame({'drug_id': prediction[0][0], 'predictions': prediction[0][1], 'date': pd.to_datetime(df_pred[['year', 'month', 'day']], infer_datetime_format=True, errors = 'coerce')})
    prediction_data = prediction_data.set_index(['date'])
    prediction_data.sort_index(inplace = True)
    # csd_prediction = ColumnDataSource(prediction_data)
    return prediction_data

def make_plot(historical_data, prediction_data, title):
    #Historical Data
    plot = figure(plot_width=800, plot_height = 800, x_axis_type = 'datetime',
                  toolbar_location = 'below')
    plot.xaxis.axis_label = 'Time'
    plot.yaxis.axis_label = 'Price ($)'
    plot.axis.axis_label_text_font_style = 'bold'
    plot.x_range = DataRange1d(range_padding = 0.0)
    plot.grid.grid_line_alpha = 0.3
    plot.title.text = title
    plot.line(x = 'date', y='nadac_per_unit', source = historical_data, line_color = 'blue', ) #plot historical data
    plot.line(x = 'date', y='predictions', source = prediction_data, line_color = 'red') #plot prediction data (line from last date/price point to date, price point for input_date above)
    return plot

def update_plot(attrname, old, new):
    ver = vselect.value
    new_hist_source = get_historical_data(src_hist, ver) #calls the function above to get the data instead of handling it here on its own
    historical_data.data = ColumnDataSource.from_df(new_hist_source)
    # new_pred_source = get_prediction_data(src_pred, ver)
    # prediction_data.data = new_pred_source.data

#Import data source
src_hist = pd.read_csv('data/historical_data.csv')
src_pred = pd.read_csv('data/pred_data.csv')

#Prep for default view
#Initialize plot with ID number
ver = 781593600
#Set the prediction date
input_date = datetime.datetime(2020, 3, 31) #Make this selectable in future
#Select-menu options
menu_options = src_pred['ndc'].astype(str) #already contains unique values
#Create select (dropdown) menu
vselect = Select(value=str(ver), title='Drug ID', options=sorted((menu_options)))

#Prep datasets for plotting
historical_data = get_historical_data(src_hist, ver)
prediction_data = get_prediction_data(src_pred, ver)

#Create a new plot with the source data
plot = make_plot(historical_data, prediction_data, "Drug Prices")

#Update the plot every time 'vselect' is changed'
vselect.on_change('value', update_plot)
controls = row(vselect)

curdoc().add_root(row(plot, controls))

更新：错误

：1）Jupyter Notebook中没有错误显示。

2）CLI显示UserWarning: Pandas doesn't allow columns to be careated via a new attribute name，引用`historical_data.data = ColumnDatasource.from_df（new_hist_source）。

最终，该图应该具有一条历史数据线，以及一条来自sklearn的预测数据线或点。它还具有一个下拉菜单，用于选择要绘制的每个项目（一次选择一个）。

Answer 1

您的update_plot是空操作，实际上不对Bokeh模型状态进行任何更改，这是更改Bokeh图的必要条件。更改散景模型状态意味着

为散景对象的属性分配新值。通常，要更新图，您将计算一个新的数据字典，然后从中设置一个现有的CDS：

source.data = new_data # plain python dict

或者，如果要从DataFame更新：
source.data = ColumnDataSource.from_df(new_df)

此外，请勿将.data从一个CDS分配给另一个：
source.data = other_source.data  # BAD

相反，您的update_plot计算一些新数据，然后将其丢弃。请注意，从任何Bokeh回调中返回任何内容都没有任何目的。回调由Bokeh库代码调用，该代码不期望或不使用任何返回值。 
最后，我认为最后的JS控制台错误都不是BokehJS产生的。