Bokeh: Quick Start - Revision history

Onnowpurbo: /* Instalasi */

2016-02-22T22:44:08Z

Instalasi

← Older revision		Revision as of 22:44, 22 February 2016
Line 11:		Line 11:
	Jika kita menggunakan Anaconda, kita dapat menggunakan		Jika kita menggunakan Anaconda, kita dapat menggunakan

			easy_install -U pip
	conda install bokeh		conda install bokeh

Line 17:		Line 18:
	Jika kita cukup Percaya Diri (PD) bahwa kita mempunyai semua dependensi yang bidiagonal seperti NumPy, Pandas, dan Redis, maka kita dapat menggunakan,		Jika kita cukup Percaya Diri (PD) bahwa kita mempunyai semua dependensi yang bidiagonal seperti NumPy, Pandas, dan Redis, maka kita dapat menggunakan,

			easy_install -U pip
	pip install bokeh		pip install bokeh


	==Instalasi Web Server==		==Instalasi Web Server==

Onnowpurbo at 03:04, 21 February 2016

2016-02-21T03:04:26Z

← Older revision		Revision as of 03:04, 21 February 2016
Line 1:		Line 1:
	Sumber: http://bokeh.pydata.org/en/latest/docs/quickstart.html		Sumber: http://bokeh.pydata.org/en/latest/docs/quickstart.html


			==Instalasi Anaconda==

			Klik [[Python: Anaconda - Install \| Cara Instalasi Anacoda]]

Onnowpurbo at 07:39, 27 November 2015

2015-11-27T07:39:49Z

← Older revision		Revision as of 07:39, 27 November 2015
Line 102:		Line 102:






	~~Jupyter Notebooks~~

	~~At this point we should mention Jupyter (formerly IPython) notebooks.~~

	Jupyter notebooks are a fantastic tool for exploratory data analysis, and they are widely used across the “PyData” community. Bokeh integrates seamlessly with Jupyter notebooks. To view the above examples in a notebook, you would only change output_file() to a call to output_notebook() instead.

	~~A large number of static examples may be viewed directly online at the Bokeh NBViewer Gallery.~~

	~~The Bokeh GitHub repository also has a number of example notebooks in the examples/plotting/notebook/ directory. After cloning the repository, navigate there and run:~~

	~~ipython notebook~~

	You can open and interact with any of the notebooks listed in the index page that automatically opens up. In particular, you might check out the interact_basic and interact_numba examples that show how Bokeh can be used together with Jupyter interactive widgets.

	==Referensi==		==Referensi==

	* http://bokeh.pydata.org/en/latest/docs/quickstart.html		* http://bokeh.pydata.org/en/latest/docs/quickstart.html

Onnowpurbo: /* Contoh Line Chart */

2015-11-27T07:27:40Z

Contoh Line Chart

← Older revision		Revision as of 07:27, 27 November 2015
Line 28:		Line 28:
	Buat file		Buat file

	vi /var/www/html/~~line-chart~~.py		vi /var/www/html/lines.py

	Isi dengan		Isi dengan
Line 48:		Line 48:

	# show the results		# show the results
	show(p)		# show(p)

			Jalankan

			cd /var/www/html
			python /var/www/html/lines.py

			Akses menggunakan browser

			http://ip-server/lines.html


			Langkah untuk membuat plot menggunakan bokeh.plotting interface adalah:


			* Siapkan data (disini menggunakan python lists).
			* Perintahkan Bokeh untuk membuat output (disini menggunakan output_file(), dengan nama file "lines.html").
	~~When you execute this script~~, ~~you will see that a new output~~ file "lines.html" ~~is created~~, ~~and that a browser automaticaly opens a new tab to display it~~. (~~For presentation purposes we have included the~~ plot ~~output directly inline in this document~~.)		* Call figure() untuk membuat plot dengan semua opsi, seperti title, tools, label dll.
			* Tambahkan renderers (disini menggunakan, Figure.line) visual dapat di custom seperti color, legend dan width dari plot.
	~~The basic steps to creating plots with the bokeh~~.~~plotting interface are:~~		* Perintahkan Bokeh untuk show() atau save() hasilnya.

	* Prepare some data (in this case plain python lists).		Langkah tiga dan empat dapat di ulang untuk membuat lebih dari satu plot. Contoh di bawah ini,
	* Tell Bokeh where to generate output (in this case using output_file(), with "lines.html" as the filename to save as).
	* Call figure() to create a plot with some overall options like title, tools and axes labels.
	* Add renderers (in this case, Figure.line) for our data, with visual customizations like colors, legends and widths to the plot.
	* Ask Bokeh to show() or save() the results.

	~~Steps three and four can be repeated to create more than one plot. See some examples of this below.~~

	~~The bokeh.plotting interface is also quite handy if we need to customize the output a bit more by adding more data series~~, ~~glyphs, logarithmic axis, etc. It’s also possible to easily combine multiple glyphs together on one plot as shown below:~~

	from bokeh.plotting import figure, output_file, show		from bokeh.plotting import figure, output_file, show

	# prepare some data		# prepare some data
	x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]		x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]

Onnowpurbo: /* Instalasi Web Server */

2015-11-27T07:19:07Z

Instalasi Web Server

← Older revision		Revision as of 07:19, 27 November 2015
Line 20:		Line 20:
	php5-gd php5-cli php5-curl mysql-client mysql-server		php5-gd php5-cli php5-curl mysql-client mysql-server

			==Contoh Line Chart==

	~~Getting Started~~		Lakukan

	Bokeh is a large library that exposes many capabilities, so this section is only a quick tour of some common Bokeh use-cases and workflows. For more detailed information please consult the full User Guide.		cd /var/www/html

	~~Let’s begin with some examples.~~		Buat file

	~~Plotting some data in basic Python lists as a~~ line chart ~~including zoom, pan, resize, save, and other tools is simple and straightforward:~~		vi /var/www/html/line-chart.py

			Isi dengan

	from bokeh.plotting import figure, output_file, show		from bokeh.plotting import figure, output_file, show

	# prepare some data		# prepare some data
	x = [1, 2, 3, 4, 5]		x = [1, 2, 3, 4, 5]

Onnowpurbo: /* Instalasi */

2015-11-27T07:12:54Z

Instalasi

← Older revision		Revision as of 07:12, 27 November 2015
Line 10:		Line 10:
	Ini akan menginstalasi secara lengkap, tanpa pusing.		Ini akan menginstalasi secara lengkap, tanpa pusing.

	Jika kita cukup Percaya Diri (PD) bahwa kita mempunyai semua dependensi yang ~~dibutuhkan~~ seperti NumPy, Pandas, dan Redis, maka kita dapat menggunakan,		Jika kita cukup Percaya Diri (PD) bahwa kita mempunyai semua dependensi yang bidiagonal seperti NumPy, Pandas, dan Redis, maka kita dapat menggunakan,

	pip install bokeh		pip install bokeh


			==Instalasi Web Server==

			sudo apt-get install apache2 php5 php5-xmlrpc php5-mysql \
			php5-gd php5-cli php5-curl mysql-client mysql-server


	Getting Started		Getting Started
Line 109:		Line 116:

	You can open and interact with any of the notebooks listed in the index page that automatically opens up. In particular, you might check out the interact_basic and interact_numba examples that show how Bokeh can be used together with Jupyter interactive widgets.		You can open and interact with any of the notebooks listed in the index page that automatically opens up. In particular, you might check out the interact_basic and interact_numba examples that show how Bokeh can be used together with Jupyter interactive widgets.





	==Referensi==		==Referensi==

	* http://bokeh.pydata.org/en/latest/docs/quickstart.html		* http://bokeh.pydata.org/en/latest/docs/quickstart.html

Onnowpurbo at 07:08, 27 November 2015

2015-11-27T07:08:32Z

← Older revision		Revision as of 07:08, 27 November 2015
Line 22:		Line 22:
	Plotting some data in basic Python lists as a line chart including zoom, pan, resize, save, and other tools is simple and straightforward:		Plotting some data in basic Python lists as a line chart including zoom, pan, resize, save, and other tools is simple and straightforward:

	from bokeh.plotting import figure, output_file, show		from bokeh.plotting import figure, output_file, show

	# prepare some data		# prepare some data
Line 61:		Line 61:
	The bokeh.plotting interface is also quite handy if we need to customize the output a bit more by adding more data series, glyphs, logarithmic axis, etc. It’s also possible to easily combine multiple glyphs together on one plot as shown below:		The bokeh.plotting interface is also quite handy if we need to customize the output a bit more by adding more data series, glyphs, logarithmic axis, etc. It’s also possible to easily combine multiple glyphs together on one plot as shown below:

	from bokeh.plotting import figure, output_file, show		from bokeh.plotting import figure, output_file, show

	# prepare some data		# prepare some data

Onnowpurbo at 07:05, 27 November 2015

2015-11-27T07:05:34Z

← Older revision		Revision as of 07:05, 27 November 2015
Line 2:		Line 2:


			==Instalasi==

	~~Quick Installation~~		Jika kita menggunakan Anaconda, kita dapat menggunakan

	~~There are a few different ways to install Bokeh.~~

	~~If you are using the~~ Anaconda ~~Python distribution (which is recommended)~~, ~~enter this command at a Bash or Windows command prompt:~~

	conda install bokeh		conda install bokeh

	This installs all the dependencies that you need to be ready to run Bokeh and we strongly recommend using it. It reduces the installation effort to nearly zero on any platform and configuration (including Windows). It also installs the examples into the examples/ subdirectory of your Anaconda (or miniconda) installation directory.		Ini akan menginstalasi secara lengkap, tanpa pusing.

	~~If you are confident that you have dependencies such as~~ NumPy, Pandas, ~~and~~ Redis ~~installed~~, ~~then you can also use pip at the command line:~~		Jika kita cukup Percaya Diri (PD) bahwa kita mempunyai semua dependensi yang dibutuhkan seperti NumPy, Pandas, dan Redis, maka kita dapat menggunakan,

	pip install bokeh		pip install bokeh

	~~Note~~

	~~The pip method does not install the examples. Clone the Git repository and look in the examples/ directory of the checkout to see examples.~~

	~~Windows users, see Notes for Windows Users in the Installation section.~~
	Getting Started		Getting Started

Onnowpurbo at 06:34, 27 November 2015

2015-11-27T06:34:04Z

← Older revision		Revision as of 06:34, 27 November 2015
Line 9:		Line 9:
	If you are using the Anaconda Python distribution (which is recommended), enter this command at a Bash or Windows command prompt:		If you are using the Anaconda Python distribution (which is recommended), enter this command at a Bash or Windows command prompt:

	conda install bokeh		conda install bokeh

	This installs all the dependencies that you need to be ready to run Bokeh and we strongly recommend using it. It reduces the installation effort to nearly zero on any platform and configuration (including Windows). It also installs the examples into the examples/ subdirectory of your Anaconda (or miniconda) installation directory.		This installs all the dependencies that you need to be ready to run Bokeh and we strongly recommend using it. It reduces the installation effort to nearly zero on any platform and configuration (including Windows). It also installs the examples into the examples/ subdirectory of your Anaconda (or miniconda) installation directory.
Line 15:		Line 15:
	If you are confident that you have dependencies such as NumPy, Pandas, and Redis installed, then you can also use pip at the command line:		If you are confident that you have dependencies such as NumPy, Pandas, and Redis installed, then you can also use pip at the command line:

	pip install bokeh		pip install bokeh

	Note		Note
Line 32:		Line 32:
	from bokeh.plotting import figure, output_file, show		from bokeh.plotting import figure, output_file, show

	# prepare some data		# prepare some data
	x = [1, 2, 3, 4, 5]		x = [1, 2, 3, 4, 5]
	y = [6, 7, 2, 4, 5]		y = [6, 7, 2, 4, 5]

	# output to static HTML file		# output to static HTML file
	output_file("lines.html", title="line plot example")		output_file("lines.html", title="line plot example")

	# create a new plot with a title and axis labels		# create a new plot with a title and axis labels
	p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')		p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')

	# add a line renderer with legend and line thickness		# add a line renderer with legend and line thickness
	p.line(x, y, legend="Temp.", line_width=2)		p.line(x, y, legend="Temp.", line_width=2)

	# show the results		# show the results
	show(p)		show(p)


Line 59:		Line 59:
	The basic steps to creating plots with the bokeh.plotting interface are:		The basic steps to creating plots with the bokeh.plotting interface are:

	Prepare some data (in this case plain python lists).		* Prepare some data (in this case plain python lists).
	Tell Bokeh where to generate output (in this case using output_file(), with "lines.html" as the filename to save as).		* Tell Bokeh where to generate output (in this case using output_file(), with "lines.html" as the filename to save as).
	Call figure() to create a plot with some overall options like title, tools and axes labels.		* Call figure() to create a plot with some overall options like title, tools and axes labels.
	Add renderers (in this case, Figure.line) for our data, with visual customizations like colors, legends and widths to the plot.		* Add renderers (in this case, Figure.line) for our data, with visual customizations like colors, legends and widths to the plot.
	Ask Bokeh to show() or save() the results.		* Ask Bokeh to show() or save() the results.

	Steps three and four can be repeated to create more than one plot. See some examples of this below.		Steps three and four can be repeated to create more than one plot. See some examples of this below.
Line 71:		Line 71:
	from bokeh.plotting import figure, output_file, show		from bokeh.plotting import figure, output_file, show

	# prepare some data		# prepare some data
	x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]		x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]
	y0 = [i**2 for i in x]		y0 = [i**2 for i in x]
	y1 = [10**i for i in x]		y1 = [10**i for i in x]
	y2 = [10(i2) for i in x]		y2 = [10(i2) for i in x]

	# output to static HTML file		# output to static HTML file
	output_file("log_lines.html")		output_file("log_lines.html")

	# create a new plot		# create a new plot
	p = figure(		p = figure(
	tools="pan,box_zoom,reset,save",		tools="pan,box_zoom,reset,save",
	y_axis_type="log", y_range=[0.001, 10**11], title="log axis example",		y_axis_type="log", y_range=[0.001, 10**11], title="log axis example",
	x_axis_label='sections', y_axis_label='particles'		x_axis_label='sections', y_axis_label='particles'
	)		)

	# add some renderers		# add some renderers
	p.line(x, x, legend="y=x")		p.line(x, x, legend="y=x")
	p.circle(x, x, legend="y=x", fill_color="white", size=8)		p.circle(x, x, legend="y=x", fill_color="white", size=8)
	p.line(x, y0, legend="y=x^2", line_width=3)		p.line(x, y0, legend="y=x^2", line_width=3)
	p.line(x, y1, legend="y=10^x", line_color="red")		p.line(x, y1, legend="y=10^x", line_color="red")
	p.circle(x, y1, legend="y=10^x", fill_color="red", line_color="red", size=6)		p.circle(x, y1, legend="y=10^x", fill_color="red", line_color="red", size=6)
	p.line(x, y2, legend="y=10^x^2", line_color="orange", line_dash="4 4")		p.line(x, y2, legend="y=10^x^2", line_color="orange", line_dash="4 4")

	# show the results		# show the results
	show(p)		show(p)


Line 114:		Line 114:
	The Bokeh GitHub repository also has a number of example notebooks in the examples/plotting/notebook/ directory. After cloning the repository, navigate there and run:		The Bokeh GitHub repository also has a number of example notebooks in the examples/plotting/notebook/ directory. After cloning the repository, navigate there and run:

	ipython notebook		ipython notebook

	You can open and interact with any of the notebooks listed in the index page that automatically opens up. In particular, you might check out the interact_basic and interact_numba examples that show how Bokeh can be used together with Jupyter interactive widgets.		You can open and interact with any of the notebooks listed in the index page that automatically opens up. In particular, you might check out the interact_basic and interact_numba examples that show how Bokeh can be used together with Jupyter interactive widgets.

Onnowpurbo: New page: Sumber: http://bokeh.pydata.org/en/latest/docs/quickstart.html Quick Installation There are a few different ways to install Bokeh. If you are using the Anaconda Python distribution (w...

2015-11-20T11:39:08Z

New page: Sumber: http://bokeh.pydata.org/en/latest/docs/quickstart.html Quick Installation There are a few different ways to install Bokeh. If you are using the Anaconda Python distribution (w...

New page

Sumber: http://bokeh.pydata.org/en/latest/docs/quickstart.html

Quick Installation

There are a few different ways to install Bokeh.

If you are using the Anaconda Python distribution (which is recommended), enter this command at a Bash or Windows command prompt:

conda install bokeh

This installs all the dependencies that you need to be ready to run Bokeh and we strongly recommend using it. It reduces the installation effort to nearly zero on any platform and configuration (including Windows). It also installs the examples into the examples/ subdirectory of your Anaconda (or miniconda) installation directory.

If you are confident that you have dependencies such as NumPy, Pandas, and Redis installed, then you can also use pip at the command line:

pip install bokeh

Note

The pip method does not install the examples. Clone the Git repository and look in the examples/ directory of the checkout to see examples.

Windows users, see Notes for Windows Users in the Installation section.
Getting Started

Bokeh is a large library that exposes many capabilities, so this section is only a quick tour of some common Bokeh use-cases and workflows. For more detailed information please consult the full User Guide.

Let’s begin with some examples.

Plotting some data in basic Python lists as a line chart including zoom, pan, resize, save, and other tools is simple and straightforward:

from bokeh.plotting import figure, output_file, show

# prepare some data
x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 5]

# output to static HTML file
output_file("lines.html", title="line plot example")

# create a new plot with a title and axis labels
p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')

# add a line renderer with legend and line thickness
p.line(x, y, legend="Temp.", line_width=2)

# show the results
show(p)

When you execute this script, you will see that a new output file "lines.html" is created, and that a browser automaticaly opens a new tab to display it. (For presentation purposes we have included the plot output directly inline in this document.)

The basic steps to creating plots with the bokeh.plotting interface are:

Prepare some data (in this case plain python lists).
Tell Bokeh where to generate output (in this case using output_file(), with "lines.html" as the filename to save as).
Call figure() to create a plot with some overall options like title, tools and axes labels.
Add renderers (in this case, Figure.line) for our data, with visual customizations like colors, legends and widths to the plot.
Ask Bokeh to show() or save() the results.

Steps three and four can be repeated to create more than one plot. See some examples of this below.

The bokeh.plotting interface is also quite handy if we need to customize the output a bit more by adding more data series, glyphs, logarithmic axis, etc. It’s also possible to easily combine multiple glyphs together on one plot as shown below:

from bokeh.plotting import figure, output_file, show

# prepare some data
x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]
y0 = [i**2 for i in x]
y1 = [10**i for i in x]
y2 = [10**(i**2) for i in x]

# output to static HTML file
output_file("log_lines.html")

# create a new plot
p = figure(
tools="pan,box_zoom,reset,save",
y_axis_type="log", y_range=[0.001, 10**11], title="log axis example",
x_axis_label='sections', y_axis_label='particles'
)

# add some renderers
p.line(x, x, legend="y=x")
p.circle(x, x, legend="y=x", fill_color="white", size=8)
p.line(x, y0, legend="y=x^2", line_width=3)
p.line(x, y1, legend="y=10^x", line_color="red")
p.circle(x, y1, legend="y=10^x", fill_color="red", line_color="red", size=6)
p.line(x, y2, legend="y=10^x^2", line_color="orange", line_dash="4 4")

# show the results
show(p)

Jupyter Notebooks

At this point we should mention Jupyter (formerly IPython) notebooks.

Jupyter notebooks are a fantastic tool for exploratory data analysis, and they are widely used across the “PyData” community. Bokeh integrates seamlessly with Jupyter notebooks. To view the above examples in a notebook, you would only change output_file() to a call to output_notebook() instead.

A large number of static examples may be viewed directly online at the Bokeh NBViewer Gallery.

The Bokeh GitHub repository also has a number of example notebooks in the examples/plotting/notebook/ directory. After cloning the repository, navigate there and run:

ipython notebook

You can open and interact with any of the notebooks listed in the index page that automatically opens up. In particular, you might check out the interact_basic and interact_numba examples that show how Bokeh can be used together with Jupyter interactive widgets.

==Referensi==

* http://bokeh.pydata.org/en/latest/docs/quickstart.html