<
html
>
<
head
>
rel
=
"stylesheet"
integrity
=
"sha384-+0n0xVW2eSR5OomGNYDnhzAbDsOXxcvSN1TPprVMTNDbiYZCxYbOOl7+AMvyTG2x"
crossorigin
=
"anonymous"
>
" rel=”stylesheet” type=”text/css”>
rel=”stylesheet” type=”text/css”>
</
head
>
<
body
>
<
ul
class
=
"nav nav-tabs"
>
<
li
class
=
"nav-item"
>
<
a
class
=
"nav-link active"
aria-current
=
"page"
href
=
"#"
>Active</
a
>
</
li
>
<
li
class
=
"nav-item"
>
<
a
class
=
"nav-link"
href
=
"#"
>Link</
a
>
</
li
>
<
li
class
=
"nav-item"
>
<
a
class
=
"nav-link"
href
=
"#"
>Link</
a
>
</
li
>
<
li
class
=
"nav-item"
>
<
a
class
=
"nav-link disabled"
href
=
"#"
tabindex
=
"-1"
aria-disabled
=
"true"
>Disabled</
a
>
</
li
>
</
ul
>
<
h1
align
=
"center"
>Data Visualization using Bokeh and Django</
h1
>
<
div
class
=
"container overflow-hidden"
>
<
div
class
=
"row gx-5"
>
<
div
class
=
"col"
>
<
div
class
=
"p-3 border bg-light"
>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.
The Bokeh project is sponsored by NumFocus
educational program, involved in development of
important tools such as NumPy, Pandas and more.
Bokeh can easily connect with these tools and
produce interactive plots, dashboards and data applications.
Features
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.
Some of the important features of Bokeh are as follows −
Flexibility
Bokeh is useful for common plotting requirements as
well as custom and complex use-cases.
Productivity
Bokeh can easily interact with other popular Pydata
tools such as Pandas and Jupyter notebook.
Interactivity
This is an important advantage of Bokeh over Matplotlib and
Seaborn, both produce static plots. Bokeh
creates interactive plots that change when the user
interacts with them. You can give your audience a
wide range of options and tools for inferring and
looking at data from various angles so that user can
perform “what if” analysis.
Powerful
By adding custom JavaScript, it is possible to generate
visualizations for specialised use-cases.
Sharable
Plots can be embedded in output of Flask or Django
enabled web applications. They can also be rendered in
Jupyter notebooks.
Open source
Bokeh is an open source project. It is distributed under
Berkeley Source Distribution (BSD) license. Its
</
div
>
</
div
>
<
div
class
=
"col"
>
<
div
class
=
"p-3 border bg-light"
>
<
h1
>Simple Bokeh Graph</
h1
>
{{ div| safe}}
</
div
>
</
div
>
</
div
>
</
div
>
integrity
=
"sha384-gtEjrD/SeCtmISkJkNUaaKMoLD0//ElJ19smozuHV6z3Iehds+3Ulb9Bn9Plx0x4"
crossorigin
=
"anonymous"
></
script
>
</
body
>
{{script| safe}}
</
html
>