Bokeh(Python for data science)

 Plotting With Bokeh

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

1) Data

Under the hood, your data is converted to Column Data Sources. You can also do this manually:

>>> 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)

2) Plotting

   >>> 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()

3) Renderers & Visual Customizations

 Glyphs

Scatter Markers

 >>> p1.circle(np.array([1,2,3]), np.array([3,2,1]),

                                            fill_color='white')

 >>> p2.square(np.array([1.5,3.5,5.5]), [1,4,3],

                                              color='blue', size=1)

    Line Glyphs

  >>> p1.line([1,2,3,4], [3,4,5,6], line_width=2)

 >>> p2.multi_line(pd.DataFrame([[1,2,3],[5,6,7]]),

                                  pd.DataFrame([[3,4,5],[3,2,1]]),

                                  color="blue")

Customized Glyphs

Selection and Non-Selection Glyphs

   >>> p = figure(tools='box_select')

   >>> p.circle('mpg', 'cyl', source=cds_df,

                              selection_color='red',

                             nonselection_alpha=0.1)

            Hover Glyphs

>>> from bokeh.models import HoverTool

  >>> hover = HoverTool(tooltips=None, mode='vline')

  >>> p3.add_tools(hover)

     Colormapping

    >>> from bokeh.models import CategoricalColorMapper

>>> color_mapper = CategoricalColorMapper( factors=['US', 'Asia', 'Europe'],

                                                                                     palette=['blue', 'red', 'green'])

 >>> p3.circle('mpg', 'cyl', source=cds_df,

                           color=dict(field='origin',

                           transform=color_mapper),

                           legend='Origin')

Legend Location

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')

Legend Orientation

>>> p.legend.orientation = "horizontal"

 >>> p.legend.orientation = "vertical"

Legend Background & Border

>>> p.legend.border_line_color = "navy"

>>> p.legend.background_fill_color = "white"

Rows & Columns Layout

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)

Grid Layout

>>> from bokeh.layouts import gridplot

>>> row1 = [p1,p2]

>>> row2 = [p3]

>>> layout = gridplot([[p1,p2],[p3]])

Tabbed 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])

Linked Plots

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)

4) Output & Export

Notebook

>>> from bokeh.io import output_notebook, show

>>> output_notebook()

HTML

Standalone HTML

 >>> from bokeh.embed import file_html

>>> from bokeh.resources import CDN

>>> html = file_html(p, CDN, "my_plot")

>>> from bokeh.io import output_file, show

>>> output_file('my_bar_chart.html', mode='cdn')

Components

>>> from bokeh.embed import components

 >>> script, div = components(p)

PNG

 >>> from bokeh.io import export_png

>>> export_png(p, filename="plot.png")

SVG

>>> from bokeh.io import export_svgs

>>> p.output_backend = "svg"

 >>> export_svgs(p, filename="plot.svg")

Show or Save Your Plots

>>> show(p1)

  >>> show(layout)

 >>> save(p1)

 >>> save(layout)