I am trying to Create a bokeh bar chart using Python. The data2 is the values
from bokeh.plotting import figure, output_file, show,hplot
from bokeh.charts import Bar
data2=[65.75, 48.400000000000006, 58.183333333333337, 14.516666666666666]
bar = Bar(values=data2,xlabel="beef,pork,fowl,fish",ylabel="Avg consumtion", width=400)
show(bar)
Error
TypeError: Bar() takes at least 1 argument (1 given)
What am I doing wrong Here?
The bokeh.charts API (including Bar) was deprecated and removed from Bokeh in 2017. It is unmaintained and unsupported and should not be used for any reason at this point. A simple bar chart can be accomplished using the well-supported bokeh.plotting API:
from bokeh.plotting import figure, show
categories = ["beef", "pork", "fowl", "fish"]
data = [65.75, 48.4, 58.183, 14.517]
p = figure(x_range=categories)
p.vbar(x=categories, top=data, width=0.9)
show(p)
For more information on the vastly improved support for bar and categorical plots in bokeh.plotting, see the extensive user guide section Handling Categorical Data
Note from Bokeh project maintainers: This answer refers to an obsolete and deprecated API. For information about creating bar charts with modern and fully supported Bokeh APIs, see the other response.
You might want to put all of your data in a data frame.
To directly plot what you want without doing that, you need to remove the "values" keyword.
bar = Bar(data2,xlabel="beef,pork,fowl,fish",ylabel="Avg consumtion", width=400)
This won't add your x-labels though. To do that you can do the following:
from bokeh.plotting import figure, output_file, show,hplot
from bokeh.charts import Bar
from pandas import DataFrame as df
data2=[65.75, 48.400000000000006, 58.183333333333337, 14.516666666666666]
myDF = df(
dict(
data=data2,
label=["beef", "pork", "fowl", "fish"],
))
bar = Bar(myDF, values="data",label="label",ylabel="Avg consumption", width=400)
show(bar)
Related
I'm attempting to connect a datatable with a multiselect widget in bokeh. I've searched around and gathered that I need to develop a function to update the data source for the data table, but I seem to have two problems.
I cannot seem to access the value of the multiselect object after I click it.
I cannot seem to push the change to the notebook after receiving the change.
Here's an example of my code:
import pandas as pd
from bokeh.io import push_notebook
from bokeh.plotting import show, output_notebook
from bokeh.layouts import row
from bokeh.models.widgets import MultiSelect, DataTable, TableColumn
from bokeh.models import ColumnDataSource
output_notebook()
df=pd.DataFrame({'year':[2000,2001,2000,2001,2000,2001,2000,2001],
'color':['red','blue','green','red','blue','green','red','blue'],
'value':[ 0,1,2,3,4,5,6,7]})
columns=[TableColumn(field=x, title=x) for x in df.columns]
source=ColumnDataSource(df)
data_table=DataTable(source=source,columns=columns)
years=[2000,2001,2000,2001,2000,2001,2000,2001]
## MultiSelect won't let me store an integer value, so I convert them to strings
multi=MultiSelect(title="Select a Year", value=['2000','2001'],options=[str(y) for y in set(years)])
def update(attr,old, new):
yr=multi.value
yr_vals=[int(y) for y in yr]
new_data=df[df.year.isin(yr_vals)]
source.data=new_data
push_notebook(handle=t)
multi.on_change('value',update)
t=show(row(multi,data_table),notebook_handle=True)
push_notebook is uni-directional. That is, you can only push changes from the IPython kernel, to the JavaScript front end. No changes from the UI are propagated back to the running Python kernel. In other words, on_change is not useful (without more work, see below) If you want that kind of interaction, there are a few options:
Use ipywidgets with push_notebook. Typically this involved the interact function to automatically generate a simple UI with callbacks that use push_notebook to update the plots, etc. based on the widget values. Just to be clear, this approach uses ipywidgets, which are not Bokeh built-in widgets. You can see a full example notebook here:
https://github.com/bokeh/bokeh/blob/master/examples/howto/notebook_comms/Jupyter%20Interactors.ipynb
Embed a Bokeh server application. The Bokeh server is what makes it possible for on_change callbacks on Bokeh widgets to function. Typically this involves making a function that defines the app (by specifying how a new document is created):
def modify_doc(doc):
df = sea_surface_temperature.copy()
source = ColumnDataSource(data=df)
plot = figure(x_axis_type='datetime', y_range=(0, 25))
plot.line('time', 'temperature', source=source)
def callback(attr, old, new):
if new == 0:
data = df
else:
data = df.rolling('{0}D'.format(new)).mean()
source.data = ColumnDataSource(data=data).data
slider = Slider(start=0, end=30, value=0, step=1, title="Smoothing by N Days")
slider.on_change('value', callback)
doc.add_root(column(slider, plot))
Then calling show on that function:
show(modify_doc)
A full example notebook is here:
https://github.com/bokeh/bokeh/blob/master/examples/howto/server_embed/notebook_embed.ipynb
(Hacky option) some people have combined CustomJS callbacks with Jupyers JS function kernel.execute to propagate values back to the kernel.
I recently picked up learning bokeh and I am completely lost making callbacks work.
What I would like to do is update the source using the PointDrawTool. It does update the plot and the table, but apparently it does not update the renderer or the source. This has me seriously confused and I'd appreciate some help.
What I have working is as follows:
from bokeh.models.glyphs import Circle
from bokeh.plotting import figure, show, output_notebook, Column, Row
from bokeh import events
from bokeh.models import DataTable, TableColumn, PointDrawTool, ColumnDataSource, CustomJS
output_notebook()
p = figure(width = 400, height = 600)
source = ColumnDataSource({
'x': [38], 'y': [-12], 'color': ['red']
})
renderer = p.circle(x='x', y='y',
source=source,
color='color',
size=10)
columns = [TableColumn(field="x", title="x"),
TableColumn(field="y", title="y"),
TableColumn(field='color', title='color')]
table = DataTable(source=source, columns=columns, editable=True, height=200)
draw_tool = PointDrawTool(renderers=[renderer],
empty_value='red')
p.add_tools(draw_tool)
p.toolbar.active_tap = draw_tool
show(Row(p,table))
Using your method of rendering the chart (show) it isn't possible to update the source for the chart (unless you write custom JavaScript to do so). In order to achieve this you would need to use the Bokeh server, as described here.
Basically, put all your code in a file called 'main.py', and then save this in a folder with your project name. Then in the terminal run
bokeh serve --show project_name
I haven't used the PointDrawTool before, but if it's a widget, you'll also need to write functions to program how the source data should be updated, using the on_click or on_change methods described here.
I am trying to add certain callbacks to a circles which are plotted on bokeh plot. Each circle is associated with certain record from columndatasource. I want to access that record whenever corresponding circle is clicked. Is there any way to add callbacks to circles in bokeh?
How can i do it?
I am using following code
fig =figure(x_range=(-bound, bound), y_range=(-bound, bound),
plot_width=800, plot_height=500,output_backend="webgl")
fig.circle(x='longitude',y='latitude',size=2,source=source,fill_color="blue",
fill_alpha=1, line_color=None)
Then you want to add an on_change callback to the selected property of the data source. Here is a minimal example. As stated above, python callbacks require the Bokeh server (that is where python callbacks actually get run, since the browser knows nothing of python), so this must be run e.g. bokeh serve --show example.py (Or, if you are in a notebook, following the pattern in this example notebook).
# example.py
from bokeh.io import curdoc
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure
source = ColumnDataSource(data=dict(x=[1,2,3], y=[4,6,5]))
p = figure(title="select a circle", tools="tap")
p.circle('x', 'y', size=25, source=source)
def callback(attr, old, new):
# This uses syntax for Bokeh >= 0.13
print("Indices of selected circles: ", source.selected.indices)
source.selected.on_change('indices', callback)
curdoc().add_root(p)
I would like to run a Bokeh App with an interactive Widget but cannot get it fully working.
My code demo.py:
# imports
import pandas as pd
from bokeh.io import curdoc
from bokeh.layouts import column
from bokeh.models import ColumnDataSource, Dropdown
from bokeh.plotting import figure
from bokeh.sampledata.iris import flowers
# Data
df = pd.DataFrame({'x': flowers['sepal_length'], 'y': flowers['sepal_width'], 'species': flowers['species']})
# Source
SPECIES = 'versicolor'
source = ColumnDataSource(df.loc[df.species == SPECIES])
# Create plots and widgets
plot = figure()
plot.circle(x= 'x', y='y', source=source)
menu = [("setosa", "setosa"), ("versicolor", "versicolor"), None, ("virginica", "virginica")]
dropdown = Dropdown(label="Dropdown species", button_type="warning", menu=menu)
# Add callback to widgets
def callback(attr, old, new):
SPECIES = dropdown.value
source.data=ColumnDataSource(df.loc[df.species == SPECIES])
dropdown.on_change('value', callback)
# Arrange plots and widgets in layouts
layout = column(dropdown, plot)
curdoc().add_root(layout)
When I run this app from the command line interface with bokeh serve --show demo.py, it returns an HTML-page with a plot. The dropdown seems to work, but the plot does not change when a value is selected from the dropdown.
Any suggestions how to fix this?
You are not assigning the correct value to source.data. The value needs to be a regular Python dict that maps column names to lists/arrays of data. There are a variety of ways to do that demonstrated in the docs and examples, but one good way is to use the from_df class method of CDS to generate the right kind of dict:
source.data = ColumnDataSource.from_df(df.loc[df.species == SPECIES])
That line makes your code work as expected.
As an FYI, your code generates an error in the server console output (as should be expected):
error handling message Message 'PATCH-DOC' (revision 1): ValueError("expected an element of ColumnData(String, Seq(Any)), got ColumnDataSource(id='44e09b5e-133b-4c1b-987b-cbf80b803401', ...)",)
As a gentle suggestion, it's always a good idea to include such errors in SO questions.
I am using Bokeh and Python 2.7
Im trying to update the Data Source to change the plot based on Select Box.
But I am not able to update the plot.
what am I doing wrong? or is there a better way?
Code:
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure, output_file, show, output_notebook
from bokeh.models.widgets import Select
from bokeh.io import curdoc
from bokeh.layouts import column, row
from bokeh.io import output_file, show
from bokeh import models
import pandas as pd
d1 = dict(x= [10,4,6,4], y = [6,2,8,10])
d2 = dict(x= [23,12,50,30], y = [5,10,23,18,12])
source = ColumnDataSource(data=d1)
p = figure()
select = Select(title="Select d", options=['d1', 'd2'])
def update_plot(attrname, old, new):
if new == 'd1':
newSource = d1
if new == 'd2':
newSource = d2
source.data = newSource
p.line(x='x', y='y',source = source)
select.on_change('value', update_plot)
layout = column(row(select, width=400), p)
curdoc().add_root(layout)
show(layout)
You need to start bokeh with the bokeh server, like this:
bokeh serve myscript.py
And then open localhost:5006 in your browser.
If you start bokeh without the server then it just creates a static html file and there is no way you can either make the page call your functions (that's why you don't see the prints) or alter the page with your python code after the initial load. From the docs:
The architecture of Bokeh is such that high-level “model objects” (representing things like plots, ranges, axes, glyphs, etc.) are created in Python, and then converted to a JSON format that is consumed by the client library, BokehJS. [...] However, if it were possible to keep the “model objects” in python and in the browser in sync with one another, then more [you could also] respond to UI and tool events generated in a browser with computations or queries using the full power of python