Introduction to bokeh

1. Make a figure
2. add glyphs.
3. show the plot in a output_file() or notebook.

1. Scatter plot.
2. Histograms.
3. line charts.
4. pie charts.
5. Hexbins.
6. area(stacked). 

Styling.

#import plotting essentials
from bokeh.plotting import figure, show, output_file
#import the data required
from bokeh.sampledata.iris import flowers

#set each specie to different color 
colormap = {'setosa':'red', 'versicolor':'green','virginica':'blue'}
colors = [colormap[x] for x in flowers['species']]

#put a figure instance
p = figure(title = 'Iris Morphology')
#styling
p.xaxis.axis_label = 'Petal Length'
p.yaxis.axis_label = 'Petal Width'

#adding glyphs
p.circle(flowers['petal_length'], flowers['petal_width'], color = colors, fill_alpha = 0.2, size =10)
#output plot into a file
output_file('iris.html', title = 'iris.py example')

#show plot
show(p)

#loading the data
flights = pd.read_csv('flights.csv', index_col = 0)

#making bins of 5 minuntes and limit the delays to [-60, +120]minutes
arr_hist, edges = np.histogram(flights['arr_delay'], bins = int(180/5), range = [-60, 120])

delays = pd.DataFrame({'arr_delay':arr_hist, 'left':edges[:-1], 'right':edges[1:]})

p = figure(plot_height = 600, plot_width = 600, title = 'Histogram of Arrival Delays', x_axis_label = 'Delay (min)', y_axis_label = 'Number of Flights')

p.quad(bottom=0, top =delays['flights'], left = delays['left'], right = delays['right'], fill_color = 'red', line_color = 'black')

output_file('delay histogram plots.html')

show(p)

import pandas as pd 
import numpy as np 
#import plotting essentials
from bokeh.plotting import figure, show, output_file
#import the data required
from bokeh.sampledata.iris import flowers

y = flowers['sepal_length']
x = np.arange(0, len(y))

p = figure(plot_width = 600, plot_height = 600, title = "line plot on sepal_length", x_axis_label = "rows", y_axis_label = "sepal_length")

p.line(x, y, line_color = "red")

output_file("line_plot.html")

show(p)

from math import pi

import pandas as pd

from bokeh.io import output_file, show
from bokeh.palettes import Category20c
from bokeh.plotting import figure
from bokeh.transform import cumsum

output_file("pie.html")

x = {
    'United States': 157,
    'United Kingdom': 93,
    'Japan': 89,
    'China': 63,
    'Germany': 44,
    'India': 42,
    'Italy': 40,
    'Australia': 35,
    'Brazil': 32,
    'France': 31,
    'Taiwan': 31,
    'Spain': 29
}

data = pd.Series(x).reset_index(name='value').rename(columns={'index':'country'})
data['angle'] = data['value']/data['value'].sum() * 2*pi
data['color'] = Category20c[len(x)]

p = figure(plot_height=350, title="Pie Chart", toolbar_location=None,
           tools="hover", tooltips="@country: @value", x_range=(-0.5, 1.0))

p.wedge(x=0, y=1, radius=0.4,
        start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
        line_color="white", fill_color='color', legend='country', source=data)

p.axis.axis_label=None
p.axis.visible=False
p.grid.grid_line_color = None

show(p)

from bokeh.palettes import Viridis256
from bokeh.util.hex import hexbin

#making random data
n = 50000
x = np.random.standard_normal(n)
y = np.random.standard_normal(n)

#make bins with the data here '0.1' is the size
bins = hexbin(x, y, 0.1)

# color map the bins by hand
color = [Viridis256[int(i)] for i in bins.counts/max(bins.counts)*255]

# match_aspect ensures neither dimension is squished, regardless of the plot size
p = figure(tools="wheel_zoom,reset", match_aspect=True, background_fill_color='#440154')
p.grid.visible = False

p.hex_tile(bins.q, bins.r, size=0.1, line_color=None, fill_color=color)

show(p)

#importing necessary libraries
import numpy as np 
import pandas as pd 
from bokeh.plotting import figure, show, output_file
from bokeh.palettes import brewer

#intialization
N = 20
cats = 10
df = pd.DataFrame(np.random.randint(10, 100, size = (N, cats))).add_prefix('y')

#making data
df_top = df.cumsum(axis=1)
df_bottom = df_top.shift(axis = 1).fillna({'y0':0})[::-1]
df_stack = pd.concat([df_top, df_bottom], ignore_index  =True)

#choosing color palette
colors = brewer['Spectral'][df_stack.shape[1]]

#for x axis
x2 = np.hstack((df.index[::-1], df.index))

#putting our figure
p = figure(x_range = (0, N-1), y_range = (0, 800))
p.grid.minor_grid_line_color = '#eeeeee'

#adding glyphs
p.patches([x2] * df_stack.shape[1], [df_stack[c].values for c in df_stack], color = colors, alpha = 0.8, line_color = None)

#show the plot
output_file('example.html')

show(p)

from bokeh.io import output_notebook, show 
from bokeh.plotting import figure 

p = figure(plot_width = 400, plot_height = 400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size = 10)

#Hide xgrid
p.xgrid.grid_line_color = None

#add transparency to ygrid and change line to dashed
p.ygrid.grid_line_alpha = 0.5
p.ygrid.grid_line_dash = [6, 4]

output_file('example.html')

show(p)

from bokeh.io import output_notebook, show 
from bokeh.plotting import figure 

p = figure(plot_width = 400, plot_height = 400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size = 10)

#Hide xgrid
p.xgrid.grid_line_color = None

#add transparency to band of ygrid and fill color
p.ygrid.band_fill_alpha = 0.1
p.ygrid.band_fill_color = 'navy'

output_file('example.html')

show(p)

#imports
from bokeh.io import output_notebook, show 
from bokeh.plotting import figure
from math import pi 
from bokeh.sampledata.glucose import data
#extracting subset 
week = data.loc['2010-10-01':'2010-10-08']
#configuring the figure
p = figure(x_axis_type = "datetime", title = "Glucose Range", plot_height = 350, plot_width = 800)
#format the date on xaxis display type
p.xaxis[0].formatter.days = "%m/%d/%Y"
#rotate the xaxis labels to 60'
p.xaxis.major_label_orientation = pi/3
#draw glyphs
p.line(week.index, week.glucose)

#show plot
output_file('example.html')

show(p)

from bokeh.io import output_notebook, show 
from bokeh.plotting import figure
p = figure(plot_width=400, plot_height=400)
p.asterisk([1,2,3,4,5], [2,5,8,2,7], size=12, color="olive")

# change just some things about the x-axes
p.xaxis.axis_label = "Temp"
p.xaxis.axis_line_width = 3
p.xaxis.axis_line_color = "red"

# change just some things about the y-axes
p.yaxis.axis_label = "Pressure"
p.yaxis.major_label_text_color = "orange"
p.yaxis.major_label_orientation = "vertical"

# change things on all axes
p.axis.minor_tick_in = -3
p.axis.minor_tick_out = 6

show(p)

#imports
from bokeh.io import output_notebook, show 
from bokeh.plotting import figure
from bokeh.models.markers import Circle
#add figure with tools = 'tap'
p = figure(plot_width = 400, plot_height = 400, tools ="tap")

#draw circles as glyphs
r =  p.circle([1,2,3,4,5],[2,5,8,2,7], size = 50, fill_alpha = 0.2, line_color = "firebrick", line_dash = [5, 1], line_width = 2)

#add selection styling
r.selection_glyph = Circle(fill_alpha =1, fill_color = "firebrick", line_color = None)
#add nonselection styling
r.nonselection_glyph = Circle(fill_alpha=0.2, fill_color = "grey", line_color = None)

#show the plot
output_file('example.html')
show(p)