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https://github.com/axil/bokeh-plot

Concise syntax for bokeh plots in jupyter notebook
https://github.com/axil/bokeh-plot

Last synced: 18 days ago
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Concise syntax for bokeh plots in jupyter notebook

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README 

        
# bokeh-plot



## Installation: 



    pip install bokeh-plot



## Usage:



To load this extension in jupyter notebook:



    %load_ext bokeh_plot



The following syntax is supported:



    plot([1,4,9])             # x is automatic 

    plot([1,4,9], '.-')       # line and dots 

    plot([1,2,3], [1,4,9])    # x and y 

    plot([1,2,3], [1,4,9], '.-')    # x, y and line style



Several plots in one figure: 






Interactive controls:



    click and drag = pan

    mouse wheel = zoom, 

    wheel on x axis = scroll horizontally

    wheel on y axis = scroll vertically



Multiple plot syntax (equivalent ways to draw it):



    x = [1,5,10]

    y1 = [1,4,9]

    y2 = [1,8,27]



    - plot(x, y1, '.-')        # solid line with dots

      plot(x, y2, '.-g')       # the second plot is green



    - plot([y1, y2])           # auto x, auto colors       



    - plot(x, [y1, y2])



    - plot([y1, y2], '.-bg')   # blue and green



    - plot([y1, y2], style=['.', '.-'], color=['b', 'g'])



    - plot(x, y1, '.-', x, y2, '.-g')



The following markers are supported so far:



    '.' dots

    '-' line

    '.-' dots+line



The following colors are supported so far:



    'b' blue

    'g' green

    'r' red

    'O' orange  (capital O to avoid clashes with 'o' for open dots)

    

NB The color specifier must go after the marker if both are present.



Legend:



    - plot([1,2,3], [1,4,9], label='plot1')

      plot([1,2,3], [2,5,10], label='plot2')



    - plot([y1, y2], label=['y1', 'y2'])



Legend location:



    - plot([1,2,3], [1,4,9], label='plot1', legend_loc='top_left')

      plot([1,2,3], [2,5,10], label='plot2')






Other legend locations:

https://docs.bokeh.org/en/latest/docs/user_guide/styling.html#location



Axes labels:

  

    - plot([1,2,3], xlabel='time', ylabel='value')

    - xlabel('time'); ylabel('value')

    - xylabels('time', 'value')



Several images side by side:



    - imshow(im1, im2)



All the images are displayed in a row. For more tricky layouts






Here `bp` stands for `bokeh.plot`, `bl` is is a shortcut for `bokeh.layouts`.

There're three common layouts: `bl.row`, `bl.column` and `bl.gridplot` (the former two accept list of figures,

the latter one accepts a list of lists of figures).



Other uses:



* `semilogx()`, `semilogy()` and `loglog()` show (semi)logarithmic plots with the same syntax as `plot()`.



* `hist(x)` displays a histogram of x



* `plot(x, y, hover=True)` displays point coordinates on mouse hover.



* `plot(x, y, vline=1, hline=1.5, vline_color='red')` in addition to the (x, y) plot displays an infinite vertical line with x=1 and custom red color and an infinite horizontal line with y=1.5 and the default pink color.



* `plot(df)` plots all columns of the dataframe as separate lines on the same figure with column names 

displayed in the legend and with index taken as the x axis values. If the legend grows too long, it can 

be hidden with `legend_loc='hide'` (new in v0.1.13):




* `show_df(df)` displays pandas dataframe as a table (new in v0.1.14):




* `imshow(a)` displays an array as an image:




Complete list of palettes: https://docs.bokeh.org/en/latest/docs/reference/palettes.html



See also a contour plot example in the bokeh gallery [page](https://docs.bokeh.org/en/latest/docs/gallery/image.html)



## Comparison to bokeh



bokeh-plot is a thin wrapper over the excellent bokeh library that cuts down the amount of boilerplate code.



The following two cells are equivalent: