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选题: 20200529 Add interactivity to your Python plots with Bokeh
sources/tech/20200529 Add interactivity to your Python plots with Bokeh.md
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[#]: collector: (lujun9972)
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[#]: translator: ( )
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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[#]: subject: (Add interactivity to your Python plots with Bokeh)
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[#]: via: (https://opensource.com/article/20/5/bokeh-python)
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[#]: author: (Shaun Taylor-Morgan https://opensource.com/users/shaun-taylor-morgan)
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Add interactivity to your Python plots with Bokeh
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======
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Plotting in Bokeh is a little more complicated than in some of the other
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plotting libraries, but there's a payoff for the extra effort.
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![Hands on a keyboard with a Python book ][1]
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In this series of articles, I'm looking at the characteristics of different Python plotting libraries by making the same multi-bar plot in each one. This time I'm focusing on [Bokeh][2] (pronounced "BOE-kay").
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Plotting in Bokeh is a little more complicated than in some of the [other plotting libraries][3], but there's a payoff for the extra effort. Bokeh is designed both to allow you to create your own interactive plots on the web _and_ to give you detailed control over how the interactivity works. I'll show this by adding a tooltip to the multi-bar plot I've been using in this series. It plots data from UK election results between 1966 and 2020.
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![A zoomed-in view on the plot][4]
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A zoomed-in view on the plot (© 2019 [Anvil][5])
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### Making the multi-bar plot
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To make the multi-bar plot, you need to massage your data a little.
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The original data looks like this:
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```
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>> print(long)
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year party seats
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0 1966 Conservative 253
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1 1970 Conservative 330
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2 Feb 1974 Conservative 297
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3 Oct 1974 Conservative 277
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4 1979 Conservative 339
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.. ... ... ...
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103 2005 Others 30
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104 2010 Others 29
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105 2015 Others 80
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106 2017 Others 59
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107 2019 Others 72
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[60 rows x 3 columns]
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```
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You can think of the data as a series of `seats` values for each possible `(year, party)` combination. That's exactly how Bokeh thinks of it. You need to make a list of `(year, party)` tuples:
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```
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# import the DataFrame shown above
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from votes import long as df
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# Get a tuple for each possible (year, party) combination
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x = [(str(r[1]['year']), r[1]['party']) for r in df.iterrows()]
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# This comes out as [('1922', 'Conservative'), ('1923', 'Conservative'), ... ('2019', 'Others')]
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```
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These will be the x-values. The y-values are simply the seats:
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```
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` y = df['seats']`
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```
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Now you have data that looks something like this:
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```
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x y
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('1966', 'Conservative') 253
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('1970', 'Conservative') 330
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('Feb 1974', 'Conservative') 297
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('Oct 1974', 'Conservative') 277
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('1979', 'Conservative') 339
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... ... ...
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('2005', 'Others') 30
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('2010', 'Others') 29
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('2015', 'Others') 80
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('2017', 'Others') 59
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('2019', 'Others') 72
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```
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Bokeh needs you to wrap your data in some objects it provides, so it can give you the interactive functionality. Wrap your x and y data structures in a `ColumnDataSource` object:
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```
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from bokeh.models import ColumnDataSource
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source = ColumnDataSource(data={'x': x, 'y': y})
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```
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Then construct a `Figure` object and pass in your x-data wrapped in a `FactorRange` object:
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```
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from bokeh.plotting import figure
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from bokeh.models import FactorRange
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p = figure(x_range=FactorRange(*x), width=2000, title="Election results")
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```
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You need to get Bokeh to create a colormap—this is a special `DataSpec` dictionary it produces from a color mapping you give it. In this case, the colormap is a simple mapping between the party name and a hex value:
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```
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from bokeh.transform import factor_cmap
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cmap = {
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'Conservative': '#0343df',
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'Labour': '#e50000',
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'Liberal': '#ffff14',
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'Others': '#929591',
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}
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fill_color = factor_cmap('x', palette=list(cmap.values()), factors=list(cmap.keys()), start=1, end=2)
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```
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Now you can create the bar chart:
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```
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` p.vbar(x='x', top='y', width=0.9, source=source, fill_color=fill_color, line_color=fill_color)`
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```
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Visual representations of data on Bokeh charts are referred to as glyphs, so you've created a set of bar glyphs.
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Tweak the details of the graph to get it looking how you want:
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```
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p.y_range.start = 0
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p.x_range.range_padding = 0.1
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p.yaxis.axis_label = 'Seats'
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p.xaxis.major_label_orientation = 1
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p.xgrid.grid_line_color = None
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```
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And finally, tell Bokeh you'd like to see your plot now:
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```
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from bokeh.io import show
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show(p)
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```
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This writes the plot to an HTML file and opens it in the default web browser. Here's the result:
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![A multi-bar plot in Bokeh][6]
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A multi-bar plot in Bokeh (© 2019 [Anvil][5])
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This already has some interactive features, such as a box zoom:
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![Bokeh's built-in box zoom][7]
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Bokeh's built-in box zoom (© 2019 [Anvil][5])
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But the great thing about Bokeh is how you can add your own interactivity. Explore that in the next section by adding tooltips to the bars.
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### Adding tooltips to the bars
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To add tooltips to the bars, you just need to create a `HoverTool` object and add it to your figure:
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```
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h = HoverTool(tooltips=[
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('Seats', '@y'),
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('(Year, Party)', '(@x)')
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])
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p.add_tools(h)
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```
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The arguments define what data is displayed on the tooltips. The variables `@y` and `@x` refer to the variables you passed into the `ColumnDataSource`. There are a few other values you could use; for example, the position of the cursor on the plot is given by `$x` and `$y` (no relation to `@x` and `@y`).
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Here's the result:
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![The election graph, now with tooltips][8]
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The election graph, now with tooltips (© 2019 [Anvil][5])
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Thanks to Bokeh's HTML output, you get the full interactive experience when you embed the plot in a web app. You can copy this example as an Anvil app [here][9] (Note: Anvil requires registration to use).
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Now you can see the reason for the extra effort of wrapping all your data in Bokeh in objects such as `ColumnDataSource`. In return, you can add interactivity with relative ease.
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### Back to simplicity: Altair
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Bokeh is one of the four most popular plotting libraries, and this series is looking into [what makes each of them special][3].
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I'm also looking at a couple of libraries that stand out for their interesting approach. Next, I'll look at [Altair][10], whose declarative API means it can make really complex plots without causing brain ache.
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* * *
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_This article is based on [How to make plots using Bokeh][5] on Anvil's blog and is reused with permission._
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/20/5/bokeh-python
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作者：[Shaun Taylor-Morgan][a]
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选题：[lujun9972][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://opensource.com/users/shaun-taylor-morgan
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[b]: https://github.com/lujun9972
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[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/python-programming-code-keyboard.png?itok=fxiSpmnd (Hands on a keyboard with a Python book )
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[2]: https://bokeh.org/
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[3]: https://opensource.com/article/20/4/plot-data-python
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[4]: https://opensource.com/sites/default/files/uploads/bokeh-closeup.png (A zoomed-in view on the plot)
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[5]: https://anvil.works/blog/plotting-in-bokeh
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[6]: https://opensource.com/sites/default/files/uploads/bokeh_0.png (A multi-bar plot in Bokeh)
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[7]: https://opensource.com/sites/default/files/uploads/bokeh-box-zoom.gif (Bokeh's built-in box zoom)
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[8]: https://opensource.com/sites/default/files/uploads/bokeh-tooltips.gif (The election graph, now with tooltips)
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[9]: https://anvil.works/build#clone:CFRUWSM6PQ6JUUXH%3dSX4SACDSXBB4UOIVEVPWXH55%7cMYTOLCU2HM5WKJYM%3d6VJKGRSF74TCCVDG5CTVDOCS
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[10]: https://altair-viz.github.io/
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