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选题: 20200529 Add interactivity to your Python plots with Bokeh

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[#]: collector: (lujun9972)
[#]: translator: ( )
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (Add interactivity to your Python plots with Bokeh)
[#]: via: (https://opensource.com/article/20/5/bokeh-python)
[#]: author: (Shaun Taylor-Morgan https://opensource.com/users/shaun-taylor-morgan)
Add interactivity to your Python plots with Bokeh
======
Plotting in Bokeh is a little more complicated than in some of the other
plotting libraries, but there's a payoff for the extra effort.
![Hands on a keyboard with a Python book ][1]
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").
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.
![A zoomed-in view on the plot][4]
A zoomed-in view on the plot (© 2019 [Anvil][5])
### Making the multi-bar plot
To make the multi-bar plot, you need to massage your data a little.
The original data looks like this:
```
>> print(long)
        year         party  seats
0       1966  Conservative    253
1       1970  Conservative    330
2   Feb 1974  Conservative    297
3   Oct 1974  Conservative    277
4       1979  Conservative    339
..       ...           ...    ...
103     2005        Others     30
104     2010        Others     29
105     2015        Others     80
106     2017        Others     59
107     2019        Others     72
[60 rows x 3 columns]
```
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:
```
    # import the DataFrame shown above
    from votes import long as df
    # Get a tuple for each possible (year, party) combination
    x = [(str(r[1]['year']), r[1]['party']) for r in df.iterrows()]
   
    # This comes out as [('1922', 'Conservative'), ('1923', 'Conservative'), ... ('2019', 'Others')]
```
These will be the x-values. The y-values are simply the seats:
```
`    y = df['seats']`
```
Now you have data that looks something like this:
```
x                               y
('1966', 'Conservative')        253
('1970', 'Conservative')        330
('Feb 1974', 'Conservative')    297
('Oct 1974', 'Conservative')    277
('1979', 'Conservative')        339
 ...      ...                   ...
('2005', 'Others')              30
('2010', 'Others')              29
('2015', 'Others')              80
('2017', 'Others')              59
('2019', 'Others')              72
```
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:
```
    from bokeh.models import ColumnDataSource
    source = ColumnDataSource(data={'x': x, 'y': y})
```
Then construct a `Figure` object and pass in your x-data wrapped in a `FactorRange` object:
```
    from bokeh.plotting import figure
    from bokeh.models import FactorRange
   
    p = figure(x_range=FactorRange(*x), width=2000, title="Election results")
```
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:
```
    from bokeh.transform import factor_cmap
    cmap = {
        'Conservative': '#0343df',
        'Labour': '#e50000',
        'Liberal': '#ffff14',
        'Others': '#929591',
    }
    fill_color = factor_cmap('x', palette=list(cmap.values()), factors=list(cmap.keys()), start=1, end=2)
```
Now you can create the bar chart:
```
`    p.vbar(x='x', top='y', width=0.9, source=source, fill_color=fill_color, line_color=fill_color)`
```
Visual representations of data on Bokeh charts are referred to as glyphs, so you've created a set of bar glyphs.
Tweak the details of the graph to get it looking how you want:
```
    p.y_range.start = 0
    p.x_range.range_padding = 0.1
    p.yaxis.axis_label = 'Seats'
    p.xaxis.major_label_orientation = 1
    p.xgrid.grid_line_color = None
```
And finally, tell Bokeh you'd like to see your plot now:
```
   from bokeh.io import show
   show(p)
```
This writes the plot to an HTML file and opens it in the default web browser. Here's the result:
![A multi-bar plot in Bokeh][6]
A multi-bar plot in Bokeh (© 2019 [Anvil][5])
This already has some interactive features, such as a box zoom:
![Bokeh's built-in box zoom][7]
Bokeh's built-in box zoom (© 2019 [Anvil][5])
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.
### Adding tooltips to the bars
To add tooltips to the bars, you just need to create a `HoverTool` object and add it to your figure:
```
    h = HoverTool(tooltips=[
        ('Seats', '@y'),
        ('(Year, Party)', '(@x)')
    ])
    p.add_tools(h)
```
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`).
Here's the result:
![The election graph, now with tooltips][8]
The election graph, now with tooltips (© 2019 [Anvil][5])
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).
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.
### Back to simplicity: Altair
Bokeh is one of the four most popular plotting libraries, and this series is looking into [what makes each of them special][3].
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.
* * *
_This article is based on [How to make plots using Bokeh][5] on Anvil's blog and is reused with permission._
--------------------------------------------------------------------------------
via: https://opensource.com/article/20/5/bokeh-python
作者：[Shaun Taylor-Morgan][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://opensource.com/users/shaun-taylor-morgan
[b]: https://github.com/lujun9972
[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 )
[2]: https://bokeh.org/
[3]: https://opensource.com/article/20/4/plot-data-python
[4]: https://opensource.com/sites/default/files/uploads/bokeh-closeup.png (A zoomed-in view on the plot)
[5]: https://anvil.works/blog/plotting-in-bokeh
[6]: https://opensource.com/sites/default/files/uploads/bokeh_0.png (A multi-bar plot in Bokeh)
[7]: https://opensource.com/sites/default/files/uploads/bokeh-box-zoom.gif (Bokeh's built-in box zoom)
[8]: https://opensource.com/sites/default/files/uploads/bokeh-tooltips.gif (The election graph, now with tooltips)
[9]: https://anvil.works/build#clone:CFRUWSM6PQ6JUUXH%3dSX4SACDSXBB4UOIVEVPWXH55%7cMYTOLCU2HM5WKJYM%3d6VJKGRSF74TCCVDG5CTVDOCS
[10]: https://altair-viz.github.io/