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How to colour code points using Bokeh with gmap

I currently have a gmap displaying gps points, however, I was hoping there was a way to colour code my GPS points based on which month they were recorded ? I have looked around online but am struggling to implement it into my own code. My dataset consists of GPS points collected throughout 2017, with a localDate index (in datetime format), and a longitude and latitude:
2017-11-12 |5.043978|118.715237
Bokeh and gmap code:
def plot(lat, lng, zoom=10, map_type='roadmap'):
gmap_options = GMapOptions(lat=lat, lng=lng,
map_type=map_type, zoom=zoom)
# the tools are defined below:
hover = HoverTool(
tooltips = [
# #price refers to the price column
# in the ColumnDataSource.
('Date', '#{Local Date}{%c}'),
('Lat', '#Lat'),
('Lon', '#Lon'),
],
formatters={'#{Local Date}': 'datetime'}
)
# below we replaced 'hover' (the default hover tool),
# by our custom hover tool
p = gmap(api_key, gmap_options, title='Malaysia',
width=bokeh_width, height=bokeh_height,
tools=[hover, 'reset', 'wheel_zoom', 'pan'])
source = ColumnDataSource(day2017Averageddf)
center = p.circle('Lon', 'Lat', size=4, alpha=0.5,
color='yellow', source=source)
show(p)
return p
p = plot(Lat, Lon, map_type='satellite')

The base idea is to pass the colors to the color keyword in p.circle(). You are using one color, but you could create also a list of colors with the correct length and implement your own logic or you could make use of a mapper.
The code below is a copy from the original documentation about mappers.
from bokeh.models import ColumnDataSource
from bokeh.palettes import Spectral6
from bokeh.plotting import figure, output_notebook, show
from bokeh.transform import linear_cmap
output_notebook()
x = [1,2,3,4,5,7,8,9,10]
y = [1,2,3,4,5,7,8,9,10]
#Use the field name of the column source
mapper = linear_cmap(field_name='y', palette=Spectral6 ,low=min(y) ,high=max(y))
source = ColumnDataSource(dict(x=x,y=y))
p = figure(width=300, height=300, title="Linear Color Map Based on Y")
p.circle(x='x', y='y', line_color=mapper,color=mapper, fill_alpha=1, size=12, source=source)
color_bar = ColorBar(color_mapper=mapper['transform'], width=8)
p.add_layout(color_bar, 'right')
show(p)
To come back to you problem. If the items in your Local Date column are of type pd.Timestamp, you can create a column "month" by this line
day2017Averageddf["month"] = day2017Averageddf["Local Date"].month
and use it for the mapper.

python bokeh: update scatter plot colors on callback

I only started to use Bokeh recently. I have a scatter plot in which I would like to color each marker according to a certain third property (say a quantity, while the x-axis is a date and the y-axis is a given value at that point in time).
Assuming my data is in a data frame, I managed to do this using a linear color map as follows:
min_q = df.quantity.min()
max_q = df.quantity.max()
mapper = linear_cmap(field_name='quantity', palette=palettes.Spectral6, low=min_q, high=max_q)
source = ColumnDataSource(data=get_data(df))
p = figure(x_axis_type="datetime")
p.scatter(x="date_column", y="value", marker="triangle", fill_color=mapper, line_color=None, source=source)
color_bar = ColorBar(color_mapper=mapper['transform'], width=8, location=(0,0))
p.add_layout(color_bar, 'right')
This seems to work as expected. Below is the plot I get upon starting the bokeh server.
Then I have a callback function update() triggered upon changing value in some widget (a select or a time picker).
def update():
# get new df (according to new date/select)
df = get_df()
# update min/max for colormap
min_q = df.quantity.min()
max_q = df.quantity.max()
# I think I should not create a new mapper but doing so I get closer
mapper = linear_cmap(field_name='quantity', palette=palettes.Spectral6 ,low=min_q, high=max_q)
color_bar.color_mapper=mapper['transform']
source.data = get_data(df)
# etc
This is the closest I could get. The color map is updated with new values, but it seems that the colors of the marker still follow the original pattern. See picture below (given that quantity I would expect green, but it is blue as it still seen as < 4000 as in the map of the first plot before the callback).
Should I just add a "color" column to the data frame? I feel there is an easier/more convenient way to do that.
EDIT: Here is a minimal working example using the answer by bigreddot:
from bokeh.io import curdoc
from bokeh.layouts import column
from bokeh.plotting import figure
from bokeh.models import Button, ColumnDataSource, ColorBar, HoverTool
from bokeh.palettes import Spectral6
from bokeh.transform import linear_cmap
import numpy as np
x = [1,2,3,4,5,7,8,9,10]
y = [1,2,3,4,5,7,8,9,10]
z = [1,2,3,4,5,7,8,9,10]
source = ColumnDataSource(dict(x=x, y=y, z=z))
#Use the field name of the column source
mapper = linear_cmap(field_name='z', palette=Spectral6 ,low=min(y) ,high=max(y))
p = figure(plot_width=300, plot_height=300, title="Linear Color Map Based on Y")
p.circle(x='x', y='y', line_color=mapper,color=mapper, fill_alpha=1, size=12, source=source)
color_bar = ColorBar(color_mapper=mapper['transform'], width=8, location=(0,0))
p.add_tools(HoverTool(tooltips="#z", show_arrow=False, point_policy='follow_mouse'))
p.add_layout(color_bar, 'right')
b = Button()
def update():
new_z = np.exp2(z)
mapper = linear_cmap(field_name='z', palette=Spectral6 ,low=min(new_z), high=max(new_z))
color_bar.color_mapper=mapper['transform']
source.data = dict(x=x, y=y, z=new_z)
b.on_click(update)
curdoc().add_root(column(b, p))
Upon update, the circles will be colored according to the original scale: everything bigger than 10 will be red. Instead, I would expect everything blue until the last 3 circle on tops that should be colored green yellow and red respectively.

It's possible that is a bug, feel free to open a GitHub issue.
That said, the above code does not represent best practices for Bokeh usage, which is: always make the smallest update possible. In this case, this means setting new property values on the existing color transform, rather than replacing the existing color transform.
Here is a complete working example (made with Bokeh 1.0.2) that demonstrates the glyph's colormapped colors updating in response to the data column changing:
from bokeh.io import curdoc
from bokeh.layouts import column
from bokeh.plotting import figure
from bokeh.models import Button, ColumnDataSource, ColorBar
from bokeh.palettes import Spectral6
from bokeh.transform import linear_cmap
x = [1,2,3,4,5,7,8,9,10]
y = [1,2,3,4,5,7,8,9,10]
z = [1,2,3,4,5,7,8,9,10]
#Use the field name of the column source
mapper = linear_cmap(field_name='z', palette=Spectral6 ,low=min(y) ,high=max(y))
source = ColumnDataSource(dict(x=x, y=y, z=z))
p = figure(plot_width=300, plot_height=300, title="Linear Color Map Based on Y")
p.circle(x='x', y='y', line_color=mapper,color=mapper, fill_alpha=1, size=12, source=source)
color_bar = ColorBar(color_mapper=mapper['transform'], width=8, location=(0,0))
p.add_layout(color_bar, 'right')
b = Button()
def update():
new_z = np.exp2(z)
# update the existing transform
mapper['transform'].low=min(new_z)
mapper['transform'].high=max(new_z)
source.data = dict(x=x, y=y, z=new_z)
b.on_click(update)
curdoc().add_root(column(b, p))
Here is the original plot:
And here is the update plot after clicking the button

Python Bokeh tool - How to display hovertool with datetime formatter xaxis from bokeh?

I using the bokeh vbar chart tool for my energy datas. If I use the tuple(string,string,..) for xaxis then it successfully worked. But I use the datetimetickformatter for xaxis then hover tool never display.
My sample code is here:
from bokeh.io import show, output_file
from bokeh.models import ColumnDataSource, DatetimeTickFormatter,HoverTool
from bokeh.plotting import figure
from datetime import datetime
output_file("bar_colormapped.html")
dt1=datetime(2018,8,1)
dt2=datetime(2018,8,2)
dt3=datetime(2018,8,3)
dt4=datetime(2018,8,4)
dt5=datetime(2018,8,5)
dt6=datetime(2018,8,6)
fruits = [dt1,dt2,dt4,dt5,dt6]
counts = [5, 3, 4, 4, 6]
source = ColumnDataSource(data=dict(fruits=fruits, counts=counts))
tooltips=[
("val", "#counts")
]
p = figure(plot_height=350, toolbar_location=None, title="Fruit Counts",x_axis_type='datetime',tooltips=tooltips)
p.vbar(x='fruits', top='counts', width=0.9, source=source)
p.xaxis.formatter=DatetimeTickFormatter(
minutes=["%M"],
hours=["%H:%M"],
days=["%d/%m/%Y"],
months=["%m/%Y"],
years=["%Y"],
)
p.xgrid.grid_line_color = None
p.y_range.start = 0
p.y_range.end = 9
p.legend.orientation = "horizontal"
p.legend.location = "top_center"
show(p)

This is in the documentation:
https://docs.bokeh.org/en/latest/docs/user_guide/tools.html#formatting-tooltip-fields
Presumably in your specific case, something like:
hover = HoverTool(tooltips=[('date', '#fruits{%F}'), ('val', '#counts')],
formatters=dict(fruits='datetime'))
p.add_tools(hover)
Also your bars are far too thin for hit testing. The units on a datetime scale is milliseconds since epoch, but your range covers many months. To the bars need to be much wider to show up on that scale. E.g. width=10000000 yields:

How to draw a circle plot the LinearColorMapper using python Bokeh

With the following code,
from bokeh.plotting import figure, show, output_file
from bokeh.sampledata.iris import flowers
colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'}
colors = [colormap[x] for x in flowers['species']]
p = figure(title = "Iris Morphology")
p.xaxis.axis_label = 'Petal Length'
p.yaxis.axis_label = 'Petal Width'
p.circle(flowers["petal_length"], flowers["petal_width"],
color=colors, fill_alpha=0.2, size=10)
output_file("iris.html", title="iris.py example")
show(p)
I can make a circle plot where I color the species:
But what I want to do is to color all the point based on range of
value in petal_length.
I tried this code but fail:
from bokeh.models import LinearColorMapper
exp_cmap = LinearColorMapper(palette='Viridis256', low = min(flowers["petal_length"]), high = max(flowers["petal_length"]))
p.circle(flowers["petal_length"], flowers["petal_width"],
fill_color = {'field' : flowers["petal_lengh"], 'transform' : exp_cmap})
output_file("iris.html", title="iris.py example")
show(p)
And also in the final desired plot, how can I put the color bar that
show the range of values and the assigned value. Something like this:
I'm using Python 2.7.13.

To answer your first part, there was a small typo (petal_lengh instead of petal_length) but more importantly, using the bokeh.ColumnDataSource will solve your problem (I tried to do it without CDS and only got column errors):
from bokeh.plotting import figure, show, output_file
from bokeh.sampledata.iris import flowers
from bokeh.models import LinearColorMapper
from bokeh.models import ColumnDataSource
p = figure(title = "Iris Morphology")
p.xaxis.axis_label = "Petal Length"
p.yaxis.axis_label = "Petal Width"
source = ColumnDataSource(flowers)
exp_cmap = LinearColorMapper(palette="Viridis256",
low = min(flowers["petal_length"]),
high = max(flowers["petal_length"]))
p.circle("petal_length", "petal_width", source=source, line_color=None,
fill_color={"field":"petal_length", "transform":exp_cmap})
# ANSWER SECOND PART - COLORBAR
# To display a color bar you'll need to import
# the `bokeh.models.ColorBar` class and pass it your mapper.
from bokeh.models import ColorBar
bar = ColorBar(color_mapper=exp_cmap, location=(0,0))
p.add_layout(bar, "left")
show(p)
See also: https://github.com/bokeh/bokeh/blob/master/examples/plotting/file/color_data_map.py

The colormapper transform refers to a column name and does not accept actual literal lists of data. So all the data needs to be in a Bokeh ColumDataSource and the plotting funcs all need to refer to the column names. Fortunately this is straightforward:
p.circle("petal_length", "petal_width", source=flowers, size=20,
fill_color = {'field': 'petal_length', 'transform': exp_cmap})
Directions for legends outside the plot area are documented here:
https://docs.bokeh.org/en/latest/docs/user_guide/styling.html#outside-the-plot-area

interacting with multi_line in bokeh

Trying to plot 2 lines in Bokeh and update them simultaneously based on inputs from 2 sliders. When I use multi_line, the lines do not update.
Using ipython notebook:
%matplotlib inline
import numpy as np
from ipywidgets import interact
from bokeh.models import Line, ColumnDataSource
from bokeh.plotting import figure, show
from bokeh.io import output_notebook
Define 2 simple functions that give me my 2 lines over the same x-values:
def mu(q,tau,c):
p = np.maximum((tau-2*q-c),0)
return p*q
def mu_d(q,tau,c):
mask = (tau-q-c)<0
payoff = .25*(tau-q-c)**2
payoff[mask]=0
return payoff
Now make the Bokeh plot:
x = np.linspace(0, .6, 200)
y = mu(x,1,.1)
y1 = mu_d(x,1,.1)
output_notebook()
source = ColumnDataSource(data=dict(x=x, y=y, y1=y1))
p = figure(title="simple line example", plot_height=300, plot_width=600, y_range=(0, .25))
# p.line(x, y, source=source, alpha=.5, color="red", line_width=2) #this case works
# p.line(x, y1, source=source, alpha=.5, color="red", line_width=2) #this case does not
p.multi_line([x,x], [y,y1], source=source, alpha=.5, color=["red","blue"], line_width=2) # neither does this
def update(tau=1, c=.1):
source.data['y'] = mu(x,tau,c)
source.data['y1'] = mu_d(x,tau,c)
source.push_notebook()
show(p)
And the slider:
interact(update, tau=(0,1, 0.1), c=(0,.5, 0.1))
To debug things, I've been playing with plotting single lines. Things work fine if I plot only (x,y) as in the first case (commented out). When I try the second case, the line (x,y1) turns into (x,y) when the slider updates - even when I've restarted the kernel and commented out the unused source.data lines (bug?). I've printed the mu_d() output within the update command and things look nice there, but they don't update in the figure.
Anyone else encountered this? All tools (ipython, bokeh, etc.) are the latest version as in the Anaconda dist.

This isn't multi-line, but works. Would be nice if this could be vectorized, so that any number of lines could be manipulated at once without writing a ton of code.
source1 = ColumnDataSource(data=dict(x=qgrid, y=mugrid))
source2 = ColumnDataSource(data=dict(x=qgrid, y=mudgrid))
p.line(qgrid, mugrid, source=source1, legend='mu(q)',line_color="blue",alpha=.6)
p.line(qgrid, mudgrid,source=source2,legend='mu_d(q)',line_color="green",alpha=.5)
p.legend.label_text_font = "times"
def update(tau=1, c=.1):
source1.data['y'] = mu(x,tau,c)
source2.data['y'] = mu_d(x,tau,c)
source1.push_notebook()
source2.push_notebook()
show(p)