Bokeh is an interactive Python visualization library that targets modern web browsers for presentation. It aims to provide elegant and concise construction of novel graphics in D3.js style, with high-performance interactivity over large or streaming datasets. Bokeh can help create interactive plots, dashboards, and data applications. It outputs JSON files that serve as input for the BokehJS JavaScript library. While it has bindings for other languages, this discussion will focus on Bokeh's Python capabilities, which include basic and advanced charting features. Bokeh can also display plots from libraries like Matplotlib, Seaborn, and ggplot.

1. Interactive Date Plotting With Bokeh
Kaustuv Deolal

2. Bokeh !! Well what is that ?

3. Well he would say
Bokeh is a Python interactive
visualization library that targets
modern web browsers for
presentation. Its goal is to provide
elegant, concise construction of novel
graphics in the style of D3.js, and to
extend this capability with high-
performance interactivity over very
large or streaming datasets. Bokeh can
help anyone who would like to quickly
and easily create interactive plots,
dashboards, and data applications.

4. In Rancho’s words !
“A pretty amazing plotting
library with tons of features”

5. Bokeh is simply not confined
to Python. It has bindings in
different languages. These
languages output a Json file
which serves as an input for
BokehJS , which is a
JavaScript library
Since this is PyCon we will
be talking about Bokeh
Python only !!

8. Output features
File Notebook Server .Here we show only file

9.  Plotting with Bokeh

10.  Charts !!

11.  Basic Charts Code

12.  Charts Continued……

14.  Bokeh can display plots from other python
visualization libraries
 Plots from Matplotlib, Seaborn, ggplot can be
displayed using Bokeh
 bokeh.mpl function is used to display plots
from these
 to_bokeh() uses bokeh to display Matplotlib
figure.

16.  Yes Flask can be integrated with Bokeh
 It does make altogether more dynamic
 If you have an external web service that you can
communicate with to control your data and your
app behaviour then you can actually achieve very
powerful functionalities

17.  Bokeh has seamless integration with any web framework. Bokeh Flask is
an example for that
 The primary issue is not whether or not pixels are painted onto the
screen via a web page. For instance, one could write some python code
which outputs an HTML file that embeds a Matplotlib PNG image along
with a carefully-crafted old-school imagemap via the <map> tag.
 The key difference is who the libraries are made for.
 D3 requires reasonable knowledge of javascript, and also quite a bit of
learning of how the D3 visualization engine itself works. Bokeh, on the
other hand, targets users who are interested in writing Python or R or
other non-Javascript languages, but who still want interactive graphics in
the browser.


18.  Matplotlib is too much work for creating
decent looking graphs. And is too verbose.
Graphs can be made more aesthetic with less
line of code
 Seaborn is another plotting library , but is
again based on matplotlib
 Pygal can generate interactive plots but is
again not customizable