```
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)

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

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

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


Renderers & Visual Customizations

Output and Export

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

Learn Before

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