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

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

```
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

To convert a Pandas `DataFrame` into a Bokeh `ColumnDataSource`, pass the DataFrame as an argument to the `ColumnDataSource` constructor:

```python
import numpy as np
import pandas as pd
from bokeh.models import ColumnDataSource

# Create a sample Pandas DataFrame
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']
)

# Convert the DataFrame to Bokeh ColumnDataSource
cds_df = ColumnDataSource(df)
```

Learn Before

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