# 5.1 Basic Plotting
## Creating Figures
Note that Bokeh plots created using the `bokeh.plotting` interface comes with a default set of tools, and default visual styles. Later we will learn **Styling Visual Attributes** for information about how to customize the visual style of plots.
### 1. Scatter and Markers
All the markers have the same set of properties: `x`, `y`, `size`, and `angle` (radians by default).
```
# Standard import
from bokeh.plotting import figure, output_file, show
output_notebook()
```
```
# set the figure size and title
p = figure(plot_width=400, plot_height=400,title="Simple Scatter Plot")
# add a circle renderer with a size, color, and alpha
p.circle([2, 2, 3, 4, 5], [6, 7, 5, 4, 5], size=40, color="firebrick", alpha=0.7)
show(p)
```
{% hint style="info" %}
**There are lots of marker types available in Bokeh:**
{% endhint %}
| | | |
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### 2. Line Graphs
#### Single line and step line
Here is an example to generate a single line glyph from one-dimensional sequences of *x* and *y* points using the `line()` glyph method and how to draw discrete steps between data points with `step()` method.
```
from bokeh.layouts import row # create a multiple layouts
p1 = figure(plot_width=400, plot_height=400,title = 'Simple Line')
p1.line([1, 2, 3, 4, 5], [6, 7, 10, 9, 8], line_width=3,color = 'royalblue')
p2 = figure(plot_width=400, plot_height=400,title = 'Step Line')
p2.step([1, 2, 3, 4, 5], [6, 7, 10, 9, 8], line_width=3,color = 'firebrick', mode='center')
show(row(p1,p2)) # show two figures in a row
```
#### Multiple lines and stacked lines
Sometimes it is useful to plot multiple lines all at once. Then we can use`multi_line()`to accomplish. In some instances, it is desirable to stack lines that are aligned on a common index (e.g. time series of percentages). Then we use `vline_stack()` or `hline_stack().`
```
from bokeh.models import ColumnDataSource # create a simple dataset in Bokeh
source = ColumnDataSource(data=dict(
x=[1, 2, 3, 4, 5],
y1=[1, 2, 4, 3, 4],
y2=[3, 4, 2, 5, 5],
))
```
```
p1 = figure(plot_width=400, plot_height=400, title ='Multiple Lines')
p1.multi_line([[2, 3, 4], [3, 4, 5, 6]], [[3, 4, 5], [6, 7, 8, 3]],
color=["firebrick", "royalblue"], line_width=3)
p2 = figure(plot_width=400, plot_height=400, title ='Stacked Lines')
p2.vline_stack(['y1', 'y2'], x='x',line_width = 4, source=source)
show(row(p1,p2))
```
### 3. Bars
#### Vertical Bar and Horizontal Bar
When drawing rectangular bars (often representing intervals) it is often more convenient to have coordinates that are a hybrid of the two systems above.
* to draw vertical bars by specifying a (center) x-coordinate, width, and top and bottom endpoints, use the [`vbar()`](https://docs.bokeh.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.vbar) function.
* to draw horizontal bars by specifying a (center) y-coordinate, height, and left and right endpoints, use the [`hbar()`](https://docs.bokeh.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.hbar) function.
```
p1 = figure(plot_width=400, plot_height=400,title ='Vertical Bar')
p1.vbar(x=[1, 2, 3], width=0.4, bottom=0,
top=[2, 2.5, 3.8], color="salmon")
p2 = figure(plot_width=400, plot_height=400,title ='Horizontal Bar')
p2.hbar(y=[1, 2, 3], height=0.4, left=0,
right=[2, 2.5, 3.8], color="lightblue")
show(row(p1,p2))
```
#### Stacked Bar
```
source = ColumnDataSource(data=dict(
y=[1, 2, 3, 4, 5],
x1=[1, 2, 4, 3, 4],
x2=[1, 4, 2, 2, 3],
))
p = figure(plot_width=400, plot_height=400,title = 'Stacked Bars')
p.hbar_stack(['x1', 'x2'], y='y', height=0.7,
color=("navy", "lightblue"), source=source)
show(p)
```
### 4. Directed Areas
Directed areas are filled regions between two series that share a common index. For instance, a vertical directed area has one x coordinate array, and two y coordinate arrays, y1 and y2, which will be filled between.
* A single directed area between two aligned series can be created in the vertical direction with `varea()` or in the horizontal direction with `harea()`
* The stacked area can be accomplished with the `varea_stack()` and `harea_stack()`
#### Single Area and Stacked Areas
```
source = ColumnDataSource(data=dict(
x=[1, 2, 3, 4, 5],
y1=[1, 2, 4, 3, 4],
y2=[1, 4, 2, 2, 3],
))
p1 = figure(plot_width=400, plot_height=400,title ='Single Areas')
p1.varea(x='x', y1='y1', y2='y2',source = source, color = 'royalblue')
p2 = figure(plot_width=400, plot_height=400,title ='Stacked Areas')
p2.varea_stack(['y1', 'y2'], x='x',
color=("royalblue", "lightgrey"), source=source)
show(row(p1,p2))
```
### 5. Polygons
Below is an example that shows how to generate a single polygonal patch glyph from one- dimensional sequences of *x* and *y* points using the `patch()` method and how to plot multiple polygonal patches all at once using `patches()`
```
p1 = figure(plot_width=400, plot_height=400, title = 'Single Patch')
# add a patch renderer with an alpha an line width
p1.patch([1, 2, 3, 4,5], [3,2,3,4,3], color = 'salmon', alpha=0.8, line_width=2)
p2 = figure(plot_width=400, plot_height=400,title = 'Multiple Patches')
p2.patches([[1, 3, 2], [3, 4, 6, 6]], [[2, 1, 4], [4, 7, 8, 5]],
color=["Salmon", "cyan"], alpha=[0.8, 0.5])
show(row(p1,p2))
```
### 6. Wedges and Arcs
```
p1 = figure(plot_width=400, plot_height=400,title = 'Annular Wedge')
p1.annular_wedge(x=[1, 2, 3], y=[2, 2, 2], inner_radius=0.1, outer_radius=0.25,
start_angle=0.4, end_angle=4.8, color="cyan", alpha=0.7)
p2 = figure(plot_width=400, plot_height=400,title = 'Annulus')
p2.annulus(x=[1, 2, 3], y=[3, 2, 1], inner_radius=0.1, outer_radius=0.25,
color="orange", alpha=0.8)
show(row(p1,p2))
```
### 7. Combining Glyphs and Setting Range
* Combining multiple glyphs on a single plot is a matter of calling more than one glyph method on a single `Figure`
* By default, Bokeh will attempt to automatically set the data bounds of plots to fit snugly around the data. Sometimes, you may need to set a plot’s range explicitly. Then we use `x_range` and `y_range`
```
x = [1, 2, 3, 4, 5]
y = [3, 6, 4, 7,6 ]
p1 = figure(plot_width=400, plot_height=400,title = 'Line + Scatter')
# add both a line and circles on the same plot
p1.line(x, y, line_width=3,color = 'orange')
p1.circle(x, y, fill_color="white", size=12)
p2 = figure(plot_width=400, plot_height=400, x_range=(0,5), y_range = (0,7),title = 'Setting Ranges')
p2.line(x, y, line_width=3,color = 'orange')
show(row(p1,p2))
```
As we can see from the graph, the left one combines the line chart and circle chart to show the trends. Also, the X-axis and Y-axis of the left one do not start with 0. We set the range in the right one and make sure the graph has not been misinterpreted.
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