bokeh plots just bring up a blank window

But it's possible to use "inline" resources, which means the BokehJS library is included directly in the HTML output that Bokeh (the python library) generates. The easiest way to to do this is to set the environment variable:

BOKEH_RESOURCES = inline

Suggestion : 2

Bokeh is a data visualization library for Python. Unlike Matplotlib and Seaborn, they are also Python packages for data visualization, Bokeh renders its plots using HTML and JavaScript. Hence, it proves to be extremely useful for developing web based dashboards.,By adding custom JavaScript, it is possible to generate visualizations for specialised use-cases.,Bokeh primarily converts the data source into a JSON file which is used as input for BokehJS, a JavaScript library, which in turn is written in TypeScript and renders the visualizations in modern browsers.,The Bokeh Python library, and libraries for Other Languages such as R, Scala, and Julia, primarily interacts with BokehJS at a high level. A Python programmer does not have to worry about JavaScript or web development. However, one can use BokehJS API, to do pure JavaScript development using BokehJS directly.

Generally, above packages are installed automatically when Bokeh is installed using Python’s built-in Package manager PIP as shown below −

pip3 install bokeh

If you are using Anaconda distribution, use conda package manager as follows −

conda install bokeh

To verify if Bokeh has been successfully installed, import bokeh package in Python terminal and check its version −

>>>
import bokeh
   >>>
   bokeh.__version__ '1.3.4'

Next, set up two numpy arrays where second array is sine value of first.

import numpy as np
import math
x = np.arange(0, math.pi * 2, 0.05)
y = np.sin(x)

To obtain a Bokeh Figure object, specify the title and x and y axis labels as below −

p = figure(title = "sine wave example", x_axis_label = 'x', y_axis_label = 'y')

Suggestion : 3

Before experimenting with the Lasso Select, however, click the Bokeh plot pop-out icon to pop out the figure so that you can definitely see everything that you're doing.,Bokeh is an interactive data visualization library for Python, and other languages, that targets modern web browsers for presentation. It can create versatile, data-driven graphics and connect the full power of the entire Python data science stack to create rich, interactive visualizations.,It's time to practice adding buttons to your interactive visualizations. Your job in this exercise is to create a button and use its on_click() method to update a plot.,Real Python: Interactive Data Visualization in Python With Bokeh by Christopher Bailey

import pandas as pd
from pprint
import pprint as pp
from itertools
import combinations
from pathlib
import Path
import requests
import numpy as np
from bokeh.io
import output_notebook, curdoc # output_file
from bokeh.plotting
import figure, show
from bokeh.sampledata.iris
import flowers
from bokeh.sampledata.iris
import flowers as iris_df
from bokeh.models
import ColumnDataSource, HoverTool, CategoricalColorMapper, Slider, Column, Select
from bokeh.models
import CheckboxGroup, RadioGroup, Toggle, Button
from bokeh.models.widgets
import Tabs, Panel
from bokeh.layouts
import row, column, gridplot, widgetbox
from bokeh.palettes
import Spectral6
from bokeh.themes
import Theme
import yaml

output_notebook()

pd.set_option('max_columns', 200)
pd.set_option('max_rows', 300)
pd.set_option('display.expand_frame_repr', True)

def create_dir_save_file(dir_path: Path, url: str):
   ""
"
Check
if the path exists and create it
if it does not.
Check
if the file exists and download it
if it does not.
""
"
if not dir_path.parents[0].exists():
   dir_path.parents[0].mkdir(parents = True)
print(f 'Directory Created: {dir_path.parents[0]}')
else:
   print('Directory Exists')

if not dir_path.exists():
   r = requests.get(url, allow_redirects = True)
open(dir_path, 'wb').write(r.content)
print(f 'File Created: {dir_path.name}')
else:
   print('File Exists')

data_dir = Path('data/2020-03-15_interactive_data_visualization_with_bokeh')
images_dir = Path('Images/2020-03-15_interactive_data_visualization_with_bokeh')

# AAPL Stock
aapl_url = 'https://assets.datacamp.com/production/repositories/401/datasets/313eb985cce85923756a128e49d7260a24ce6469/aapl.csv'
# Automobile miles per gallon
auto_url = 'https://assets.datacamp.com/production/repositories/401/datasets/2a776ae9ef4afc3f3f3d396560288229e160b830/auto-mpg.csv'
# Gapminder
gap_url = 'https://assets.datacamp.com/production/repositories/401/datasets/09378cc53faec573bcb802dce03b01318108a880/gapminder_tidy.csv'
# Blood glucose levels
glucose_url = 'https://assets.datacamp.com/production/repositories/401/datasets/edcedae3825e0483a15987248f63f05a674244a6/glucose.csv'
# Female literacy and birth rate
female_url = 'https://assets.datacamp.com/production/repositories/401/datasets/5aae6591ddd4819dec17e562f206b7840a272151/literacy_birth_rate.csv'
# Olympic medals(100 m sprint)
sprint_url = 'https://assets.datacamp.com/production/repositories/401/datasets/68b7a450b34d1a331d4ebfba22069ce87bb5625d/sprint.csv'
# State coordinates
state_url = 'https://github.com/trenton3983/DataCamp/blob/master/data/2020-03-15_interactive_data_visualization_with_bokeh/state_coordinates.xlsx?raw=true'

datasets = [aapl_url, auto_url, gap_url, glucose_url, female_url, sprint_url, state_url]
data_paths = list()

for data in datasets:
   file_name = data.split('/')[-1].replace('?raw=true', '')
data_path = data_dir / file_name
create_dir_save_file(data_path, data)
data_paths.append(data_path)

Suggestion : 4

One of the major benefits of bokeh is that it was designed from the ground up with consistency in mind, therefore most style options are a combination of the fill, line, and text style options listed below:,When HoloViews outputs bokeh plots it creates and manipulates bokeh models in the background. If at any time you need access to the underlying Bokeh representation of an object you can use the hv.render function to convert it. For example let us convert a HoloViews Image to a bokeh Figure, which will let us access and modify every aspect of the plot:,When dealing with complex multi-variate data it is often useful to explore interactions between variables across plots. HoloViews will automatically link the data sources of plots in a Layout if they draw from the same data, allowing for both linked axes and brushing.,Not also that most of these options support vectorized style mapping as described in the Style Mapping user guide. Here’s an example of HoloViews Elements using a Bokeh backend, with bokeh’s style options:

import numpy as np
import pandas as pd
import holoviews as hv
from holoviews
import dim, opts

hv.extension('bokeh')

img = hv.Image(np.random.rand(10, 10))

fig = hv.render(img)

print('Figure: ', fig)
print('Renderers: ', fig.renderers[-1].glyph)

Figure: Figure(id = '1004', ...)
Renderers: Image(id = '1037', ...)

conda install selenium phantomjs pillow

npm install - g phantomjs - prebuilt

hv.output(fig = 'png')