I'm just starting to use Bokeh. Below I create some args I use for the rect figure.
x_length = var_results.index * 5.5
Multiplying the index by 5.5 gave me more room between labels.
names = var_results.Feature.tolist() y_length = var_results.Variance y_center = var_results.Variance/2
var_results
is a Pandas dataframe that has a typical, sequential, non-repeating index. var_results
also has a column Features
that is strings of non-repeated, names, and finally it has a column Variance
which is dtype float.
r = figure(x_range = names, y_range = (-0.05,.3), active_scroll = 'wheel_zoom', x_axis_label = 'Features', y_axis_label = 'Variance') r.rect(x_length, y_center, width=1, height=y_length, color = "#ff1200") output_notebook() show(r)
I'm essentially making a bar chart with rectangles. Bokeh seems to be very customizable. But my graph looks rough around the edges, literally.
As you can see there is an ugly smudge just below the chart and above the x-axis title 'Features'. This is the label titles (technically the rectangle titles). How do I create space for and perhaps rotate to 45 degrees the labels so that they are readable and not just an overlapping mess?
Rotate X-Axis Tick Labels in Matplotlib There are two ways to go about it - change it on the Figure-level using plt. xticks() or change it on an Axes-level by using tick. set_rotation() individually, or even by using ax.
ang = xtickangle returns the rotation angle for the x-axis tick labels of the current axes as a scalar value in degrees. Positive values indicate counterclockwise rotation. Negative values indicate clockwise rotation. ang = xtickangle( ax ) uses the axes specified by ax instead of the current axes.
In order to rotate the labels e.g. by 90 degrees to the left, you can set major_label_orientation
to π/2. This can be done either when creating the axis element (as a kwarg to the axis constructor if you are using low level plotting) or also after you have created a plot/figure, for instance by:
p.xaxis.major_label_orientation = math.pi/2 # or alternatively: p.xaxis.major_label_orientation = "vertical"
See also this example in the documentation.
As an alternative to rotation, you set the orientation to a fixed value:
p.xaxis.major_label_orientation = "vertical"
should do what you want, too.
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