TableColumn() field and title seem interdependent

Might have ran across something when playing w/ DataTable() and TableColumn().

The goal was to keep a generic DataTable around to catch unknown data. TableColumn fields would be keys and known data mapped onto them in subsequent server updates.

If you toggle your title w/out also matching the field, the data won’t propagate.

I expected the cds to map to the underlying fields and treat the titles as cosmetic.

Am I crazy?

from import curdoc
from bokeh.plotting import figure, show
from bokeh.layouts import column, row
from bokeh.models import ColumnDataSource, CDSView, IndexFilter
from bokeh.models import Button, PreText, TableColumn, DataTable
from import show, output_notebook
from bokeh.resources import INLINE
import pandas as pd
import numpy as np

def bkapp(doc):
    #pretend to not know the data
    data = pd.DataFrame.from_records({'col_1': None , 'col_2': None, 'col_3': None, 'col_4':None, 'col_5':None}, index=[0])
    columns = [TableColumn(field=n, title=n) for n in data.keys()]
    #create a source and mxn table, all datasource updates w/ have to adhere to column count
    source = ColumnDataSource(data)
    data_table = DataTable(source=source, columns=columns, height=100)
    def select_file():
        #visual indicators
        data_table.visible = True
        #grab the new data
        new_data = dict(
            foo=[randint(0, 100) for i in range(3)],
            bar=[randint(0, 100) for i in range(3)],

        new_data = pd.DataFrame.from_records(new_data)
        #creater a mapper for table to update col names | not fields, just display titles, fields are preserved
        mapper = dict(itertools.zip_longest(data_table.columns, new_data.columns, fillvalue=None))
        #update the table for found data and fit vanilla cds
        for k,v in mapper.items():
            if v is not None:
                #column title and field are not independent
                k.title = v
                #k.field = v
                #if v is None add to new_data
                new_data = new_data.reindex(columns=new_data.columns.tolist() + [k.title])
        #send the data = new_data
    def remove_col():
        #remove first column
        x = data_table.columns[0]

    btn = Button(label="GetData")
    doc.add_root(column(data_table, btn))

The column field (which you not updating) tells the column which CDS column to display. But your callback is setting a new CDS data dict, with new, different column names (printed at the end of your callback):

['index', 'bar', 'baz', 'foo', 'col_4', 'col_5']

You never configured any column to look at a field (column name) “bar”. You configured the first column with field="col_1" which no longer exists in the CDS. As expected, nothing can be rendered as a result. You can either:

  • Make sure the CDS only ever has the same column names as the fields you initially configured, every time there is an update, or
  • Update the fields to match the new column names as they change.

None of this has anything to do with the titles, which can vary independently of the field, as you would expect. [1]

  1. If no title is specified, a column will default to using the field (which is not optional) as the title. ↩︎


Ah! I got lost in the reindexing and data updates. Added a rename in the cds matching loop and it works perfectly. Thanks again.

for k,v in mapper.items():
    if v is not None:
        k.title = v
        new_data.rename(columns={v:k.field}, inplace=True)
        #if v is None add to new_data
        new_data = new_data.reindex(columns=new_data.columns.tolist() + [k.title])
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