Develop Reference

bokeh line plot with string x value

I want to make out a bokeh line plot with string x-value but I just get an empty bokeh plot
sample={'A':['2012-01','2012-02','2012-03'],'B':[7,8,9]}
from bokeh.plotting import figure, output_file, show
source2 = ColumnDataSource(sample)
p = figure(width=400, height=400)
p.line(x='A',y='B',source=source2, line_width=2)
output_notebook()
show(p)

Please notice, you should always check the imports. In your example two imports are missing.
To use date representation in bokeh, you can set the x_axis_type to "datetime" but this will enable only the DatetimeFormatter. Befor this, you have to transform the date string somehow into a number. One easy option is to use pandas.to_datetime().
This tutorial shows how to enable datetime axes.
Example
import pandas as pd
from bokeh.plotting import figure, output_notebook, show
from bokeh.models import ColumnDataSource
sample={'A':[pd.to_datetime(x) for x in ['2012-01','2012-02','2012-03']],'B':[7,8,9]}
source = ColumnDataSource(sample)
p = figure(width=400, height=400, x_axis_type='datetime')
p.line(x='A', y='B', source=source, line_width=2)
output_notebook()
show(p)
Output

How to plot LabelSet outside the plot box?

I'm trying to highlight last value of a time series plot by plot its value on yaxis, as shown in this question. I prefer using LabelSet over Legend because you can precisely control the text positions and also using a data source to update it. But unfortunately, I can not find out how to draw label text outside the plot box.
Here is some code to plot LabelSet and notice how the text is only shown inside the box (66.1x is partially blocked by yaxis):
import pandas as pd
from bokeh.io import output_notebook
output_notebook()
from bokeh.plotting import figure, show
from bokeh.models import LabelSet, ColumnDataSource
#import bokeh.sampledata
#bokeh.sampledata.download()
from bokeh.sampledata.stocks import MSFT
df = pd.DataFrame(MSFT)[:50]
df["date"] = pd.to_datetime(df["date"])
p = figure(
x_axis_type="datetime", width=1000, toolbar_location='left',
title = "MSFT Candlestick", y_axis_location="right")
p.line(df.date, df.close)
ds = ColumnDataSource({'x': [df.date.iloc[-1]], 'y': [df.close.iloc[-1]], 'text': [' ' + str(df.close.iloc[-1])]})
ls = LabelSet(x='x', y='y', text='text', source=ds)
p.add_layout(ls)
show(p)
Please let me know how to show LabelSet outside the box, Thanks

Bokeh scatter plot: is it possible to overlay a line colored by category?

I have a dataframe that details sales of various product categories vs. time. I'd like to make a "line and marker" plot of sales vs. time, per category. To my surprise, this appears to be very difficult in Bokeh.
The scatter plot is easy. But then trying to overplot a line of sales vs. date with the same source (so I can update both scatter and line plots in one go when the source updates) and in such a way that the colors of the line match the colors of the scatter plot markers proves near impossible.
Minimal reproducible example with contrived data:
import pandas as pd
df = pd.DataFrame({'Date':['2020-01-01','2020-01-02','2020-01-01','2020-01-02'],\
'Product Category':['shoes','shoes','grocery','grocery'],\
'Sales':[100,180,21,22],'Colors':['red','red','green','green']})
df['Date'] = pd.to_datetime(df['Date'])
from bokeh.io import output_notebook
output_notebook()
from bokeh.io import output_file, show
from bokeh.plotting import figure
source = ColumnDataSource(df)
plot = figure(x_axis_type="datetime", plot_width=800, toolbar_location=None)
plot.scatter(x="Date",y="Sales",size=15, source=source, fill_color="Colors", fill_alpha=0.5, \
line_color="Colors",legend="Product Category")
for cat in list(set(source.data['Product Category'])):
tmp = source.to_df()
col = tmp[tmp['Product Category']==cat]['Colors'].values[0]
plot.line(x="Date",y="Sales",source=source, line_color=col)
show(plot)
Here's what it looks like, which is clearly wrong:
Here's what I want and don't know how to make:
Can Bokeh not make such plots, where scatter markers and lines have the same color per category, with a legend?

With bokeh it is often helpful to first think about the visualisation you want and then structuring the data source appropriately. You want two lines, on per category, the x axis is time and y axis is the sales. Then a natural way to structure your data source is the following:
df = pd.DataFrame({'Date':['2020-01-01','2020-01-02'],
'Shoe Sales':[100, 180],
'Grocery Sales': [21, 22]
})
from bokeh.io import output_notebook
output_notebook()
from bokeh.io import output_file, show
from bokeh.plotting import figure
source = ColumnDataSource(df)
plot = figure(x_axis_type="datetime", plot_width=800, toolbar_location=None)
categories = ["Shoe Sales", "Grocery Sales"]
colors = {"Shoe Sales": "red", "Grocery Sales": "green"}
for category in categories:
plot.scatter(x="Date",y=category,size=15, source=source, fill_color=colors[category], legend=category)
plot.line(x="Date",y=category,source=source, line_color=colors[category])
show(plot)

The solutions is to group your data. Then you can plot lines for each group.
Minimal Example
import pandas as pd
from bokeh.plotting import figure, show, output_notebook
output_notebook()
df = pd.DataFrame({'Date':['2020-01-01','2020-01-02','2020-01-01','2020-01-02'],
'Product Category':['shoes','shoes','grocery','grocery'],
'Sales':[100,180,21,22],'Colors':['red','red','green','green']})
df['Date'] = pd.to_datetime(df['Date'])
plot = figure(x_axis_type="datetime",
plot_width=400,
plot_height=400,
toolbar_location=None
)
plot.scatter(x="Date",
y="Sales",
size=15,
source=df,
fill_color="Colors",
fill_alpha=0.5,
line_color="Colors",
legend_field="Product Category"
)
for color in df['Colors'].unique():
plot.line(x="Date", y="Sales", source=df[df['Colors']==color], line_color=color)
show(plot)
Output

Cannot import name 'Scatter' from 'bokeh.plotting'

I am trying to represent the data using the bokeh scatter.
Here is my code:
from bokeh.plotting import Scatter, output_file, show import pandas
df=pandas.Dataframe(colume["X","Y"])
df["X"]=[1,2,3,4,5,6,7]
df["Y"]=[23,43,32,12,34,54,33]
p=Scatter(df,x="X",y="Y", title="Day Temperature measurement", xlabel="Tempetature", ylabel="Day")
output_file("File.html")
show(p)
The Output should look like this:
Expected Output
The error is:
ImportError Traceback (most recent call
> last) <ipython-input-14-1730ac6ad003> in <module>
> ----> 1 from bokeh.plotting import Scatter, output_file, show
> 2 import pandas
> 3
> 4 df=pandas.Dataframe(colume["X","Y"])
> 5
ImportError: cannot import name 'Scatter' from 'bokeh.plotting'
(C:\Users\LENOVO\Anaconda3\lib\site-packages\bokeh\plotting__init__.py)
I had also found that the Scatter is no longer maintained now. Is there is any way to use it?
Also which alternative do I have to represent the data same as the Scatter using any another python libraries?
Using older version of Bokeh will resolve this issue?

Scatter (with a capital S) has never been part of bokeh.plotting. It used to be a part of the old bokeh.charts API that was removed several years ago. However, it is not needed at all to create basic scatter plots, since all the glyph methods in bokeh.plotting (e.g circle, square) are all implicitly scatter-type functions to begin with:
from bokeh.plotting import figure, show
import pandas as pd
df = pd.DataFrame({"X" :[1,2,3,4,5,6,7],
"Y": [23,43,32,12,34,54,33]})
p = figure(x_axis_label="Tempetature", y_axis_label="Day",
title="Day Temperature measurement")
p.circle("X", "Y", size=15, source=df)
show(p)
Which yields:
You can also just pass the data directly to circle as in the other answer.
If you want to do fancier things, like map the marker type based on a column there is also a plot.scatter (lower case s) methods on the figure:
from bokeh.plotting import figure, show
from bokeh.sampledata.iris import flowers
from bokeh.transform import factor_cmap, factor_mark
SPECIES = ['setosa', 'versicolor', 'virginica']
MARKERS = ['hex', 'circle_x', 'triangle']
p = figure(title = "Iris Morphology")
p.xaxis.axis_label = 'Petal Length'
p.yaxis.axis_label = 'Sepal Width'
p.scatter("petal_length", "sepal_width", source=flowers, legend_field="species", fill_alpha=0.4, size=12,
marker=factor_mark('species', MARKERS, SPECIES),
color=factor_cmap('species', 'Category10_3', SPECIES))
show(p)
which yields:

If you look up "scatter" in the docs, you'll find
Scatter Markers
To scatter circle markers on a plot, use the circle() method of Figure:
from bokeh.plotting import figure, output_file, show
# output to static HTML file
output_file("line.html")
p = figure(plot_width=400, plot_height=400)
# add a circle renderer with a size, color, and alpha
p.circle([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=20, color="navy", alpha=0.5)
# show the results
show(p)
To work with dataframes, just pass in the columns like df.X and df.Y to the x and y args.
from bokeh.plotting import figure, show, output_file
import pandas as pd
df = pd.DataFrame(columns=["X","Y"])
df["X"] = [1,2,3,4,5,6,7]
df["Y"] = [23,43,32,12,34,54,33]
p = figure()
p.scatter(df.X, df.Y, marker="circle")
#from bokeh.io import output_notebook
#output_notebook()
show(p) # or output to a file...

Incomplete bokeh plot

I have a pandas dataframe of 10 columns and trying to get bar plot using Bokeh.
The HTML file has the complete plot when I use plot_width=10000.
However when I increase the plot width(so that there is space between x axes values) to 30000, the plot does not fill beyond 2010. Here is the complete code. Please suggest the way forward.
from bokeh.palettes import Viridis6 as palette
from bokeh.transform import factor_cmap
from bokeh.models import ColumnDataSource,FactorRange,HoverTool
from bokeh.palettes import Spectral6
from flask import Flask, request, render_template, session, redirect,send_file
import numpy as np
import pandas as pd
from bokeh.plotting import figure, show, output_file,save
from bokeh.embed import components,file_html
from bokeh.resources import CDN
from bokeh.layouts import row,column
from bokeh.core.properties import value
dates = pd.date_range('20050101', periods=3900)
df = pd.DataFrame(np.random.randn(3900, 10), index=dates, columns=list('ABCDEFGHIJ'))
s = df.resample('M').mean().stack()
s.index = [s.index.get_level_values(0).strftime('%Y-%m-%d'),s.index.get_level_values(1)]
x = s.index.values
l1=list(s.index.levels[1])
counts = s.values
source = ColumnDataSource(data=dict(x=x, counts=counts))
p = figure(x_range=FactorRange(*x), plot_height=250,plot_width=30000, title='Plotting data',
toolbar_location=None, tools="")
p.vbar(x='x', top='counts', width=1, source=source, line_color="white")
p.y_range.start = s.values.min()
p.y_range.end = s.values.max()
p.x_range.range_padding = 0.01
p.y_range.range_padding = 0.01
p.xaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None
output_file('test_plot.html')
save([p])
show(p)

This works fine for me with Bokeh 1.0.4 and OSX/Safari. I suspect this is a limitation/issue with the underlying HTML Canvas implementation in whatever browser you are using, in which case there is nothing we can do about it. The only suggestions I can make are to split the plot up into smaller subplots, or use a different browser (or possibly different version of the same browser)