Glyphs

1. Selection and Non-Selections Glyphs

2. Hover Glyphs

3. Colormapping

Customized Glyphs

Inside Plot Area
```
p.legend.location = 'bottom_left'
```
Outside Plot Area
```
from bokeh.models import Legend
r1 = p2.asterisk(np.array([1,2,3]), np.array([3,2,1])
r2 = p2.line([1,2,3,4], [3,4,5,6])
legend = Legend(items=[("One" ,[p1, r1]),("Two",[r2])],

                    location=(0, -30))
p.add_layout(legend, 'right')
```

Legen Location

```
p.legend.orientation = "horizontal"
p.legend.orientation = "vertical"

Legend Orientation

```
p.legend.border_line_color = "navy"
p.legend.background_fill_color = "white"
 

Legend Background and Border

Rows
```
from bokeh.layouts import row
layout = row(p1,p2,p3)
```
Columns
```
from bokeh.layouts import columns
layout = column(p1,p2,p3)
```
Nesting Rows & Columns
```
layout = row(column(p1,p2), p3)
```

Rows and Columns Layout

```
from bokeh.models.widgets import Panel, Tabs
tab1 = Panel(child=p1, title="tab1")
tab2 = Panel(child=p2, title="tab2")
layout = Tabs(tabs=[tab1, tab2])
 ```

Tabbed Layout

Linked Axes
```
p2.x_range = p1.x_range
p2.y_range = p1.y_range
```
Linked Brushing
```
p4 = figure(plot_width = 100,
           tools='box_select,lasso_select')
p4.circle('mpg', 'cyl', source=cds_df)
p5 = figure(plot_width = 200,
           tools='box_select,lasso_select')
p5.circle('mpg', 'hp', source=cds_df)
layout = row(p4,p5)
```

Linked Plots

1. Glyphs

2. Customized Glyphs

3. Legend Location

4. Legend Orientation

5. Legend Background and Border

6. Rows & Columns Layout

7. Grid Layout

8. Tabbed Layout

9. Linked Plots


Michigan State University

The Python interactive visualization library Bokeh enables high-performance visual presentation of large datasets in modern web browsers.

Bokeh’s mid-level general purpose bokeh.plotting interface is centered around two main components: data and glyphs.

The basic steps to creating plots with the bokeh.plotting interface are:

1. Prepare some data:

Python lists, NumPy arrays, Pandas DataFrames and other sequences of values

2. Create a new plot

3. Add renderers for your data, with visual customizations
 
4. Specify where to generate the output

5. Show or save the results

```
from bokeh.plotting import figure
from bokeh.io import output_file, show
x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 5]
p = figure(title="simple line example",
p.line(x, y, legend="Temp.", line_width=2) 
           x_axis_label='x',
           y_axis_label='y')
output_file("lines.html") 
 show(p) 
```

Plotting with Bokeh

```
import numpy as np
import pandas as pd
df = pd.DataFrame(np.array([[33.9,4,65, 'US'],
                                  [32.4,4,66, 'Asia'],
                                  [21.4,4,109, 'Europe']]), 
                 columns=['mpg','cyl', 'hp', 'origin'],
                 index=['Toyota', 'Fiat', 'Volvo'])

 from bokeh.models import ColumnDataSource
cds_df = ColumnDataSource(df)

Data: How to manually convert your data to column data

```
from bokeh.plotting import figure
p1 = figure(plot_width=300, tools='pan,box_zoom')
p2 = figure(plot_width=300, plot_height=300,
               x_range=(0, 8), y_range=(0, 8))
p3 = figure()

Plotting

Renderers & Visual Customizations

Output and Export

```
show(p1)              
show(layout)
save(p1)             
save(layout)

Learn Before

Related

Learn After