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The following commit(s) were added to refs/heads/master by this push:
new 88dd28f [ZEPPELIN-4589]. Improve Python tutorial notes
88dd28f is described below
commit 88dd28f7800c5b57288dec169c569790043cfc34
Author: Jeff Zhang <zjf...@apache.org>
AuthorDate: Wed Feb 5 15:13:35 2020 +0800
[ZEPPELIN-4589]. Improve Python tutorial notes
### What is this PR for?
In this PR, I added 4 python tutorial notes:
* IPython Basic (cover the basic ipython usage in Zeppelin)
* IPython Visualization Tutorial (cover the how to do visualization via
IPython in Zeppelin)
* Keras Binary Classification (IMDB) (cover keras usage in Zeppelin)
* Matplotlib (Python, PySpark) (How to use matplotlib in vanilla python
interpreter)
### What type of PR is it?
[ Documentation ]
### Todos
* [ ] - Task
### What is the Jira issue?
* https://issues.apache.org/jira/browse/ZEPPELIN-4589
### How should this be tested?
* CI pass
### Screenshots (if appropriate)
### Questions:
* Does the licenses files need update? No
* Is there breaking changes for older versions? No
* Does this needs documentation? No
Author: Jeff Zhang <zjf...@apache.org>
Closes #3630 from zjffdu/ZEPPELIN-4589 and squashes the following commits:
8391c2508 [Jeff Zhang] [ZEPPELIN-4589]. Improve Python tutorial notes
---
.../Python Tutorial/IPython Basic_2EYDJKFFY.zpln | 638 ++++++++++++
.../IPython Visualization Tutorial_2F1S9ZY8Z.zpln | 1093 ++++++++++++++++++++
...ras Binary Classification (IMDB)_2F2AVWJ77.zpln | 507 +++++++++
.../Matplotlib (Python, PySpark)_2C2AUG798.zpln | 288 +++---
4 files changed, 2386 insertions(+), 140 deletions(-)
diff --git a/notebook/Python Tutorial/IPython Basic_2EYDJKFFY.zpln
b/notebook/Python Tutorial/IPython Basic_2EYDJKFFY.zpln
new file mode 100644
index 0000000..85c0aba
--- /dev/null
+++ b/notebook/Python Tutorial/IPython Basic_2EYDJKFFY.zpln
@@ -0,0 +1,638 @@
+{
+ "paragraphs": [
+ {
+ "title": "Introduction",
+ "text": "%md\n\nIPython is more powerful than the default python
interpreter with extra functionality. You can use IPython with Python2 or
Python3 which depends on which python you set `zeppelin.python`. This tutorial
will teach you how to use IPython and what kind of fancy feature IPython
provides for you. ",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:16:03.242",
+ "config": {
+ "tableHide": false,
+ "editorSetting": {
+ "language": "text",
+ "editOnDblClick": false,
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+ },
+ "colWidth": 12.0,
+ "editorMode": "ace/mode/text",
+ "fontSize": 9.0,
+ "editorHide": true,
+ "results": {},
+ "enabled": true,
+ "title": true
+ },
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+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003cp\u003eIPython is more powerful than
the default python interpreter with extra functionality. You can use IPython
with Python2 or Python3 which depends on which python you set
\u003ccode\u003ezeppelin.python\u003c/code\u003e. This tutorial will teach you
how to use IPython and what kind of fancy feature IPython provides for
you.\u003c/p\u003e\n\n\u003c/div\u003e"
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580883128399_1705920905",
+ "id": "20180118-105449_1130638270",
+ "dateCreated": "2020-02-05 14:12:08.399",
+ "dateStarted": "2020-02-05 14:12:44.213",
+ "dateFinished": "2020-02-05 14:12:45.639",
+ "status": "FINISHED"
+ },
+ {
+ "title": "",
+ "text": "%md\n\n# Prerequisite\n\nFirst you need to use Zeppelin 0.8.0
or afterwards. You need to install the following 2 packages to make IPython
work in Jupyter.\n\n* jupyter `pip install jupyter`\n* grpcio `pip install
grpcio`\n* protobuf `pip install protobuf`\n\nIf you have anaconda installed,
then you just need to install `grpcio` and `protobuf` as Jupyter is already
included in anaconda.\n\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:45:28.518",
+ "config": {
+ "tableHide": false,
+ "editorSetting": {
+ "language": "text",
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+ "editorMode": "ace/mode/text",
+ "fontSize": 9.0,
+ "editorHide": true,
+ "results": {},
+ "enabled": true
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003ch1\u003ePrerequisite\u003c/h1\u003e\n\u003cp\u003eFirst
you need to use Zeppelin 0.8.0 or afterwards. You need to install the
following 2 packages to make IPython work in
Jupyter.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ejupyter
\u003ccode\u003epip install
jupyter\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003egrpcio
\u003ccode\u003epip install
grpcio\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eprotobuf \u003 [...]
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580883128401_-91751836",
+ "id": "20180118-105734_546968647",
+ "dateCreated": "2020-02-05 14:12:08.401",
+ "dateStarted": "2020-02-05 14:15:52.966",
+ "dateFinished": "2020-02-05 14:15:52.978",
+ "status": "FINISHED"
+ },
+ {
+ "title": "",
+ "text": "%md\n\n# How to use IPython\n\nAfter you get the above
prerequisite installed, you can use IPython in Zeppelin via `%python.ipython`
or `%spark.ipyspark`. If you want to make IPython as your default interpreter
via `%python` or `%spark.pyspark`, then you can configure
`zeppelin.python.useIPython` as `true` in python\u0027s interpreter setting or
`zeppelin.pyspark.useIPython` in spark\u0027s interpreter setting. So that
Zeppelin will use IPython as the default python interp [...]
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+ "dateUpdated": "2020-02-05 14:16:52.364",
+ "config": {
+ "tableHide": false,
+ "editorSetting": {
+ "language": "text",
+ "editOnDblClick": false,
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+ "editorHide": true,
+ "results": {},
+ "enabled": true
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003ch1\u003eHow to use
IPython\u003c/h1\u003e\n\u003cp\u003eAfter you get the above prerequisite
installed, you can use IPython in Zeppelin via
\u003ccode\u003e%python.ipython\u003c/code\u003e or
\u003ccode\u003e%spark.ipyspark\u003c/code\u003e. If you want to make IPython
as your default interpreter via \u003ccode\u003e%python\u003c/code\u003e or
\u003ccode\u003e%spark.pyspark\u003c/code\u003e, then you can configure \ [...]
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580883128402_-1251111399",
+ "id": "20180118-112614_44484642",
+ "dateCreated": "2020-02-05 14:12:08.402",
+ "dateStarted": "2020-02-05 14:16:50.898",
+ "dateFinished": "2020-02-05 14:16:50.907",
+ "status": "FINISHED"
+ },
+ {
+ "title": "",
+ "text": "%md\n\n# IPython Help\n\nThere\u0027re 2 approaches to for
getting python help in IPython. \n* Use `?` after the python object,\n* Use
`help` function",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:12:08.402",
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+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003ch1\u003eIPython
Help\u003c/h1\u003e\n\u003cp\u003eThere\u0026rsquo;re 2 approaches to for
getting python help in IPython.\u003cbr/\u003e* Use
\u003ccode\u003e?\u003c/code\u003e after the python object,\u003cbr/\u003e* Use
\u003ccode\u003ehelp\u003c/code\u003e function\u003c/p\u003e\n\u003c/div\u003e"
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580883128402_-1077704099",
+ "id": "20180118-111958_1561079624",
+ "dateCreated": "2020-02-05 14:12:08.402",
+ "status": "READY"
+ },
+ {
+ "title": "",
+ "text": "%python.ipython\n\nimport sys\n\nsys?",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:17:03.465",
+ "config": {
+ "editorSetting": {
+ "language": "python",
+ "editOnDblClick": false,
+ "completionKey": "TAB",
+ "completionSupport": true
+ },
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+ "results": {},
+ "enabled": true,
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+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "\u001b[0;31mType:\u001b[0m
module\n\u001b[0;31mString form:\u001b[0m \u003cmodule \u0027sys\u0027
(built-in)\u003e\n\u001b[0;31mDocstring:\u001b[0m \nThis module provides
access to some objects used or maintained by the\ninterpreter and to functions
that interact strongly with the interpreter.\n\nDynamic objects:\n\nargv --
command line arguments; argv[0] is the script pathname if known\npath -- module
search path; path[0] is the script directory, else \u0027 [...]
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580883128402_-688763859",
+ "id": "20180118-112246_474555672",
+ "dateCreated": "2020-02-05 14:12:08.403",
+ "status": "READY"
+ },
+ {
+ "title": "",
+ "text": "%python.ipython\n\nimport sys\n\nhelp(sys)",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:12:08.403",
+ "config": {
+ "editorSetting": {
+ "language": "scala",
+ "editOnDblClick": false,
+ "completionKey": "TAB"
+ },
+ "colWidth": 12.0,
+ "editorMode": "ace/mode/scala",
+ "fontSize": 9.0,
+ "results": {},
+ "enabled": true
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "Help on built-in module sys:\n\nNAME\n sys\n\nMODULE
REFERENCE\n https://docs.python.org/3.6/library/sys\n \n The following
documentation is automatically generated from the Python\n source files. It
may be incomplete, incorrect or include features that\n are considered
implementation detail and may vary between Python\n implementations. When
in doubt, consult the module reference at the\n location listed
above.\n\nDESCRIPTION\n This modu [...]
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580883128403_-2022546282",
+ "id": "20180118-112905_1540839273",
+ "dateCreated": "2020-02-05 14:12:08.403",
+ "status": "READY"
+ },
+ {
+ "title": "",
+ "text": "%md\n\n# IPython magic function\n\nAll the IPython magic
functions are avalible in Zeppelin, here\u0027s one example of `%timeit`, for
the complete IPython magic functions, you can check the
[link](http://ipython.readthedocs.io/en/stable/interactive/magics.html)
here.\n\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:12:08.404",
+ "config": {
+ "tableHide": false,
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+ "language": "markdown",
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+ "fontSize": 9.0,
+ "editorHide": true,
+ "results": {},
+ "enabled": true
+ },
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+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003ch1\u003eIPython magic
function\u003c/h1\u003e\n\u003cp\u003eAll the IPython magic functions are
avalible in Zeppelin, here\u0026rsquo;s one example of
\u003ccode\u003e%timeit\u003c/code\u003e, for the complete IPython magic
functions, you can check the \u003ca
href\u003d\"http://ipython.readthedocs.io/en/stable/interactive/magics.html\"\u003elink\u003c/a\u003e
here.\u003c/p\u003e\n\u003c/div\u003e"
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+ ]
+ },
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+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580883128404_-1445116404",
+ "id": "20180118-112924_1945597351",
+ "dateCreated": "2020-02-05 14:12:08.404",
+ "status": "READY"
+ },
+ {
+ "title": "",
+ "text": "%python.ipython\n\n%timeit range(1000)",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:12:08.405",
+ "config": {
+ "editorSetting": {
+ "language": "python",
+ "editOnDblClick": false,
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+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "242 ns ± 7.71 ns per loop (mean ± std. dev. of 7 runs,
1000000 loops each)\n"
+ }
+ ]
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+ "jobName": "paragraph_1580883128405_-1093651105",
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+ "dateCreated": "2020-02-05 14:12:08.405",
+ "status": "READY"
+ },
+ {
+ "title": "",
+ "text": "%md\n\n# Tab completion\n\nTab completion, especially for
attributes, is a convenient way to explore the structure of any object you’re
dealing with. Simply type `object_name.\u003cTAB\u003e` to view the object’s
attributes. See the following snapshot of how tab completion works in IPython
Interpreter.\n![alt
text](https://user-images.githubusercontent.com/164491/34858941-3f28105a-f78e-11e7-8341-2fbfd306ba5b.gif
\"Logo Title Text 1\")\n\n\n\n",
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+ "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003ch1\u003eTab
completion\u003c/h1\u003e\n\u003cp\u003eTab completion, especially for
attributes, is a convenient way to explore the structure of any object you’re
dealing with. Simply type
\u003ccode\u003eobject_name.\u0026lt;TAB\u0026gt;\u003c/code\u003e to view the
object’s attributes. See the following snapshot of how tab completion works in
IPython Interpreter.\u003cbr/\u003e\u003cimg src\u003d\"https://user-image [...]
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+ ]
+ },
+ "apps": [],
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+ "jobName": "paragraph_1580883128405_-1429375020",
+ "id": "20180118-113333_768199475",
+ "dateCreated": "2020-02-05 14:12:08.405",
+ "status": "READY"
+ },
+ {
+ "title": "",
+ "text": "%md\n\n\n# Visualization\n\nOne big advangae of notebook is
that you can inline visualization with your code in notebook. There\u0027re
many awesome visualization libraries in Python, you can use most of them in
Zeppelin.\nRefer this
[tutorial](https://www.zepl.com/viewer/notebooks/bm90ZTovL3pqZmZkdS9lN2Q3ODNiODRkNjA0ZjVjODM1OWZlMWExZjM4OTk3Zi9ub3RlLmpzb24)
for how to use Python visualization libraries in Apache Zeppelin.\n\n\n",
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+ "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003ch1\u003eVisualization\u003c/h1\u003e\n\u003cp\u003eOne
big advangae of notebook is that you can inline visualization with your code
in notebook. There\u0026rsquo;re many awesome visualization libraries in
Python, you can use most of them in Zeppelin.\u003cbr/\u003eRefer this \u003ca
href\u003d\"https://www.zepl.com/viewer/notebooks/bm90ZTovL3pqZmZkdS9lN2Q3ODNiODRkNjA0ZjVjODM1OWZlMWExZjM4OTk3Zi9ub3RlLmpzb24\"\u003etu
[...]
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+ "dateCreated": "2020-02-05 14:12:08.405",
+ "status": "READY"
+ },
+ {
+ "title": "",
+ "text": "%md\n\n# Use ZeppelinContext \n\n`ZeppelinContext` is a utlity
class which provide the following features\n\n* Dynamic forms\n* Show DataFrame
via builtin visualization\n\n",
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visualization\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e"
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+ },
+ {
+ "title": "",
+ "text": "%python.ipython\n\nz.input(name\u003d\u0027my_name\u0027,
defaultValue\u003d\u0027hello\u0027)",
+ "user": "anonymous",
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+ "dateCreated": "2020-02-05 14:12:08.405",
+ "status": "READY"
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+ {
+ "title": "",
+ "text": "%python.ipython\n\nimport pandas as pd\ndf \u003d
pd.DataFrame({\u0027name\u0027:[\u0027a\u0027,\u0027b\u0027,\u0027c\u0027],
\u0027count\u0027:[12,24,18]})\nz.show(df)",
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diff --git a/notebook/Python Tutorial/IPython Visualization
Tutorial_2F1S9ZY8Z.zpln b/notebook/Python Tutorial/IPython Visualization
Tutorial_2F1S9ZY8Z.zpln
new file mode 100644
index 0000000..dc73cdf
--- /dev/null
+++ b/notebook/Python Tutorial/IPython Visualization Tutorial_2F1S9ZY8Z.zpln
@@ -0,0 +1,1093 @@
+{
+ "paragraphs": [
+ {
+ "title": "Overview",
+ "text": "%md\n\n\nThere\u0027re many visualization libraries availabel
in python world. Visualization is tremendous useful in notebook enviroment,
most of them could be used in Jupyter Notebook. And most of them can also be
used in Apache Zeppelin Notebook, but maybe a little difference for some
libraries. This tutorial will teach you how to use these popular visualization
libraries in Apache Zeppelin. To be noted, you need to use IPython interpreter
to make these libraries work in [...]
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+ "dateUpdated": "2020-02-05 15:12:28.523",
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+ {
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+ "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003cp\u003eThere\u0026rsquo;re many
visualization libraries availabel in python world. Visualization is tremendous
useful in notebook enviroment, most of them could be used in Jupyter Notebook.
And most of them can also be used in Apache Zeppelin Notebook, but maybe a
little difference for some libraries. This tutorial will teach you how to use
these popular visualization libraries in Apache Zeppelin. To be noted, you n
[...]
+ }
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+ "apps": [],
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+ "id": "20180117-220147_1889980489",
+ "dateCreated": "2020-02-05 14:34:42.808",
+ "dateStarted": "2020-02-05 15:09:34.095",
+ "dateFinished": "2020-02-05 15:09:34.123",
+ "status": "FINISHED"
+ },
+ {
+ "title": "",
+ "text": "%md\n\n# Matplotlib\n\nMatplotlib is a Python 2D plotting
library which produces publication quality figures in a variety of hardcopy
formats and interactive environments across platforms. The usage of Matplotlib
in Zeppelin is the same as Jupyter Notebook. The key is to put `%matplotlib
inline` before using Matplotlig. Below is one simple example, for more usage of
Matplotlib, you can refer this [link](https://matplotlib.org/).",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:09:59.174",
+ "config": {
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+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
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+ {
+ "type": "HTML",
+ "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003ch1\u003eMatplotlib\u003c/h1\u003e\n\u003cp\u003eMatplotlib
is a Python 2D plotting library which produces publication quality figures in
a variety of hardcopy formats and interactive environments across platforms.
The usage of Matplotlib in Zeppelin is the same as Jupyter Notebook. The key is
to put \u003ccode\u003e%matplotlib inline\u003c/code\u003e before using
Matplotlig. Below is one simple example, for more usa [...]
+ }
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+ "dateStarted": "2020-02-05 14:37:05.024",
+ "dateFinished": "2020-02-05 14:37:05.035",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Matplotlib Example",
+ "text": "%python.ipython\n\n%matplotlib inline\n\nimport
matplotlib.pyplot as plt\n\nplt.plot([1,2,3,4])\nplt.ylabel(\u0027some
numbers\u0027)\nplt.show()\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:34:42.810",
+ "config": {
+ "editorSetting": {
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+ "dateCreated": "2020-02-05 14:34:42.810",
+ "status": "READY"
+ },
+ {
+ "title": "",
+ "text": "%md\n\n# Pandas\n\nPandas provide high level api for
visualization on DataFrame. It uses Matplotlib for its visualization
underlying, so the usage is the same as Matplotlib. ",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:34:42.811",
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Matplotlib.\u003c/p\u003e\n\u003c/div\u003e"
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+ "status": "READY"
+ },
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+ "title": "Pandas Example",
+ "text": "%python.ipython\n\n%matplotlib inline\n\nimport pandas as
pd\nimport numpy as np\n\nts \u003d pd.Series(np.random.randn(1000),
index\u003dpd.date_range(\u00271/1/2000\u0027, periods\u003d1000))\nts \u003d
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+ "dateCreated": "2020-02-05 14:34:42.811",
+ "status": "READY"
+ },
+ {
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based on matplotlib. It provides a high-level interface for drawing attractive
statistical graphics. Its usage in Zeppelin is the same as in Jupyter. For
seaborn usage please refer this [link](https://seaborn.pydata.org/)\n",
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+ "title": "Seaborn Example",
+ "text": "%python.ipython\n\n%matplotlib inline\n\nimport seaborn as
sns\nsns.set(style\u003d\"ticks\")\n\n# Load the example dataset for
Anscombe\u0027s quartet\ndf \u003d sns.load_dataset(\"anscombe\")\n\n# Show the
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col_wrap\u003d2, ci\u003dNone, palette\u003d\"muted\", size\u003d4,\n
scatter_kws\u003d{\"s\": 50, [...]
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of graphics in Python, it is based on ggplot2. The grammar allows users to
compose plots by explicitly mapping data to the visual objects that make up the
plot.\n",
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+ "dateFinished": "2020-02-05 14:48:29.950",
+ "status": "FINISHED"
+ },
+ {
+ "text": "%python.ipython\n\n(ggplot(mtcars, aes(\u0027wt\u0027,
\u0027mpg\u0027, color\u003d\u0027factor(gear)\u0027))\n + geom_point()\n +
stat_smooth(method\u003d\u0027lm\u0027)\n + facet_wrap(\u0027~gear\u0027))",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:57:48.332",
+ "config": {
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"/Users/jzhang/anaconda3/lib/python3.7/site-packages/plotnine/scales/scale.py:93:
MatplotlibDeprecationWarning: \nThe iterable function was deprecated in
Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n if
cbook.iterable(self.breaks) and
cbook.iterable(self.labels):\n/Users/jzhang/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495:
FutureWarning: Method .ptp is deprecated and will be removed in a future
version. Use numpy.ptp in [...]
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+ {
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+ "data":
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[...]
+ },
+ {
+ "type": "TEXT",
+ "data": "\u003cggplot: (7558407260)\u003e"
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580885791251_758930573",
+ "id": "paragraph_1580885791251_758930573",
+ "dateCreated": "2020-02-05 14:56:31.251",
+ "dateStarted": "2020-02-05 14:57:36.295",
+ "dateFinished": "2020-02-05 14:57:37.319",
+ "status": "FINISHED"
+ },
+ {
+ "title": "",
+ "text": "%md\n\n# bokeh\n\n[Bokeh](https://bokeh.pydata.org/en/latest/)
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 [...]
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+ "dateUpdated": "2020-02-05 15:10:37.902",
+ "config": {
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+ "editorSetting": {
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+ "completionKey": "TAB"
+ },
+ "colWidth": 12.0,
+ "editorMode": "ace/mode/text",
+ "fontSize": 9.0,
+ "editorHide": true,
+ "results": {},
+ "enabled": true,
+ "title": false
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003ch1\u003ebokeh\u003c/h1\u003e\n\u003cp\u003e\u003ca
href\u003d\"https://bokeh.pydata.org/en/latest/\"\u003eBokeh\u003c/a\u003e 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 dataset [...]
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580884482814_1997313050",
+ "id": "20180118-092227_1380687854",
+ "dateCreated": "2020-02-05 14:34:42.814",
+ "dateStarted": "2020-02-05 14:42:33.017",
+ "dateFinished": "2020-02-05 14:42:33.092",
+ "status": "FINISHED"
+ },
+ {
+ "title": "",
+ "text": "%python.ipython\n\nfrom bokeh.io import output_notebook,
show\nfrom bokeh.plotting import figure\nimport
bkzep\n\noutput_notebook(notebook_type\u003d\u0027zeppelin\u0027)\n\nfig \u003d
figure()\nfig.line([1,2], [3,4])\nshow(fig)\n\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:00:57.563",
+ "config": {
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+ "editOnDblClick": false,
+ "completionKey": "TAB",
+ "completionSupport": true
+ },
+ "colWidth": 6.0,
+ "editorMode": "ace/mode/python",
+ "fontSize": 9.0,
+ "results": {},
+ "enabled": true
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\n \u003cdiv class\u003d\"bk-root\"\u003e\n
\u003ca href\u003d\"https://bokeh.pydata.org\"; target\u003d\"_blank\"
class\u003d\"bk-logo bk-logo-small bk-logo-notebook\"\u003e\u003c/a\u003e\n
\u003cspan id\u003d\"3452\"\u003eLoading BokehJS ...\u003c/span\u003e\n
\u003c/div\u003e\n\n"
+ },
+ {
+ "type": "HTML",
+ "data": "\u003cscript
type\u003d\"text/javascript\"\u003e\n(function(root) {\n function now() {\n
return new Date();\n }\n\n var force \u003d true;\n\n if (typeof
root._bokeh_onload_callbacks \u003d\u003d\u003d \"undefined\" || force
\u003d\u003d\u003d true) {\n root._bokeh_onload_callbacks \u003d [];\n
root._bokeh_is_loading \u003d undefined;\n }\n\n \n\n \n if (typeof
(root._bokeh_timeout) \u003d\u003d\u003d \"undefined\" || force
\u003d\u003d\u003d true) { [...]
+ },
+ {
+ "type": "HTML",
+ "data": "\n\n\n\n\n\n \u003cdiv class\u003d\"bk-root\"
id\u003d\"125e83e5-5abe-4ec2-b352-69745a9ccc93\"
data-root-id\u003d\"3453\"\u003e\u003c/div\u003e\n\n"
+ },
+ {
+ "type": "HTML",
+ "data": "\u003cscript
type\u003d\"text/javascript\"\u003e(function(root) {\n function
embed_document(root) {\n \n var docs_json \u003d
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+ ]
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+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580884482814_278965699",
+ "id": "20180118-092433_377794298",
+ "dateCreated": "2020-02-05 14:34:42.814",
+ "dateStarted": "2020-02-05 15:00:57.587",
+ "dateFinished": "2020-02-05 15:00:57.659",
+ "status": "FINISHED"
+ },
+ {
+ "text": "%python.ipython\n\nfrom bokeh.server.server import Server\nfrom
bokeh.application import Application\nfrom bokeh.application.handlers.function
import FunctionHandler\nfrom bokeh.plotting import figure,
ColumnDataSource\n\ndef make_document(doc):\n fig \u003d
figure(title\u003d\u0027Line plot!\u0027,
sizing_mode\u003d\u0027scale_width\u0027)\n fig.line(x\u003d[1, 2, 3],
y\u003d[1, 4, 9])\n\n doc.title \u003d \"Hello, world!\"\n
doc.add_root(fig)\n\n# Set up the [...]
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:03:13.813",
+ "config": {
+ "colWidth": 6.0,
+ "fontSize": 9.0,
+ "enabled": true,
+ "results": {},
+ "editorSetting": {
+ "language": "python",
+ "editOnDblClick": false,
+ "completionKey": "TAB",
+ "completionSupport": true
+ },
+ "editorMode": "ace/mode/python"
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv class\u003d\u0027bokeh_class\u0027
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src\u003d\"http://localhost:51396/autoload.js?bokeh-autoload-element\u003d3881\u0026bokeh-absolute-url\u003dhttp://localhost:51396\";
id\u003d\"3881\"\u003e\u003c/script\u003e\u003c/div\u003e\n"
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580886029142_1541635661",
+ "id": "paragraph_1580886029142_1541635661",
+ "dateCreated": "2020-02-05 15:00:29.143",
+ "dateStarted": "2020-02-05 15:02:48.866",
+ "dateFinished": "2020-02-05 15:02:48.927",
+ "status": "FINISHED"
+ },
+ {
+ "title": "",
+ "text": "%md\n\n# Holoviews\n\nHoloViews is an open-source Python
library designed to make data analysis and visualization seamless and simple.
With HoloViews, you can usually express what you want to do in very few lines
of code, letting you focus on what you are trying to explore and convey, not on
the process of plotting. Compared to bokeh, HoloViews is a high level
visualization library. Refer this [link](http://holoviews.org/) for more usage
tutorial of HoloViews.\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:53:49.481",
+ "config": {
+ "tableHide": false,
+ "editorSetting": {
+ "language": "text",
+ "editOnDblClick": false,
+ "completionKey": "TAB"
+ },
+ "colWidth": 12.0,
+ "editorMode": "ace/mode/text",
+ "fontSize": 9.0,
+ "editorHide": true,
+ "results": {},
+ "enabled": true
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003ch1\u003eHoloviews\u003c/h1\u003e\n\u003cp\u003eHoloViews
is an open-source Python library designed to make data analysis and
visualization seamless and simple. With HoloViews, you can usually express what
you want to do in very few lines of code, letting you focus on what you are
trying to explore and convey, not on the process of plotting. Compared to
bokeh, HoloViews is a high level visualization library. Refer th [...]
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580884482815_870528203",
+ "id": "20180120-131737_1287341499",
+ "dateCreated": "2020-02-05 14:34:42.815",
+ "dateStarted": "2020-02-05 14:53:46.544",
+ "dateFinished": "2020-02-05 14:53:46.571",
+ "status": "FINISHED"
+ },
+ {
+ "title": "",
+ "text": "%python.ipython\n\nimport pandas as pd\nimport numpy as
np\nimport holoviews as hv\nfrom holoviews import
opts\nhv.extension(\u0027bokeh\u0027)\n\nfrom bokeh.plotting import
figure\nfrom bokeh.io import show,output_notebook\nimport
bkzep\noutput_notebook(notebook_type\u003d\u0027zeppelin\u0027)\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:50:15.242",
+ "config": {
+ "editorSetting": {
+ "language": "python",
+ "editOnDblClick": false,
+ "completionKey": "TAB",
+ "completionSupport": true
+ },
+ "colWidth": 12.0,
+ "editorMode": "ace/mode/python",
+ "fontSize": 9.0,
+ "results": {},
+ "enabled": true
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cscript\u003e \n(function(root) {\n function now()
{\n return new Date();\n }\n\n var force \u003d true;\n\n if (typeof
root._bokeh_onload_callbacks \u003d\u003d\u003d \"undefined\" || force
\u003d\u003d\u003d true) {\n root._bokeh_onload_callbacks \u003d [];\n
root._bokeh_is_loading \u003d undefined;\n }\n\n var JS_MIME_TYPE \u003d
\u0027application/javascript\u0027;\n var HTML_MIME_TYPE \u003d
\u0027text/html\u0027;\n var EXEC_MIME_TYPE \u003d [...]
+ },
+ {
+ "type": "HTML",
+ "data": "\n\u003clink rel\u003d\"stylesheet\"
href\u003d\"https://code.jquery.com/ui/1.10.4/themes/smoothness/jquery-ui.css\"\u003e\n\u003cstyle\u003ediv.bk-hbox
{\n display: flex;\n justify-content: center;\n}\n\ndiv.bk-hbox
div.bk-plot {\n padding: 8px;\n}\n\ndiv.bk-hbox div.bk-data-table {\n
padding: 20px;\n}\n\ndiv.hololayout {\n display: flex;\n align-items:
center;\n margin: 0;\n}\n\ndiv.holoframe {\n width: 75%;\n}\n\ndiv.holowell
{\n display: flex;\n [...]
+ },
+ {
+ "type": "HTML",
+ "data": "\u003cscript\u003e function HoloViewsWidget()
{\n}\n\nHoloViewsWidget.prototype.init_slider \u003d function(init_val){\n
if(this.load_json) {\n this.from_json()\n } else {\n
this.update_cache();\n }\n}\n\nHoloViewsWidget.prototype.populate_cache \u003d
function(idx){\n this.cache[idx].innerHTML \u003d this.frames[idx];\n if
(this.embed) {\n delete this.frames[idx];\n
}\n}\n\nHoloViewsWidget.prototype.process_error \u003d
function(msg){\n}\n\nHoloViewsW [...]
+ },
+ {
+ "type": "HTML",
+ "data": "\n \u003cdiv class\u003d\"bk-root\"\u003e\n
\u003ca href\u003d\"https://bokeh.pydata.org\"; target\u003d\"_blank\"
class\u003d\"bk-logo bk-logo-small bk-logo-notebook\"\u003e\u003c/a\u003e\n
\u003cspan id\u003d\"1093\"\u003eLoading BokehJS ...\u003c/span\u003e\n
\u003c/div\u003e\n\n"
+ },
+ {
+ "type": "HTML",
+ "data": "\u003cscript
type\u003d\"text/javascript\"\u003e\n(function(root) {\n function now() {\n
return new Date();\n }\n\n var force \u003d true;\n\n if (typeof
root._bokeh_onload_callbacks \u003d\u003d\u003d \"undefined\" || force
\u003d\u003d\u003d true) {\n root._bokeh_onload_callbacks \u003d [];\n
root._bokeh_is_loading \u003d undefined;\n }\n\n \n\n \n if (typeof
(root._bokeh_timeout) \u003d\u003d\u003d \"undefined\" || force
\u003d\u003d\u003d true) { [...]
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580884482815_131583170",
+ "id": "20180120-131731_1558753626",
+ "dateCreated": "2020-02-05 14:34:42.815",
+ "dateStarted": "2020-02-05 14:50:15.270",
+ "dateFinished": "2020-02-05 14:50:17.234",
+ "status": "FINISHED"
+ },
+ {
+ "text": "%python.ipython\n\nstation_info \u003d
pd.read_csv(\u0027https://raw.githubusercontent.com/holoviz/holoviews/master/examples/assets/station_info.csv\u0027)\nstation_info.head()\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:51:57.708",
+ "config": {
+ "colWidth": 12.0,
+ "fontSize": 9.0,
+ "enabled": true,
+ "results": {},
+ "editorSetting": {
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+ "editOnDblClick": false,
+ "completionKey": "TAB",
+ "completionSupport": true
+ },
+ "editorMode": "ace/mode/python"
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv\u003e\n\u003cstyle scoped\u003e\n .dataframe
tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n
.dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe
thead th {\n text-align: right;\n }\n\u003c/style\u003e\n\u003ctable
border\u003d\"1\" class\u003d\"dataframe\"\u003e\n \u003cthead\u003e\n
\u003ctr style\u003d\"text-align: right;\"\u003e\n
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+ }
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+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
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+ "id": "paragraph_1580885453474_1167659991",
+ "dateCreated": "2020-02-05 14:50:53.474",
+ "dateStarted": "2020-02-05 14:51:57.821",
+ "dateFinished": "2020-02-05 14:52:04.924",
+ "status": "FINISHED"
+ },
+ {
+ "text": "%python.ipython\n\nscatter \u003d hv.Scatter(station_info,
\u0027services\u0027, \u0027ridership\u0027)\nscatter",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:52:34.586",
+ "config": {
+ "colWidth": 6.0,
+ "fontSize": 9.0,
+ "enabled": true,
+ "results": {},
+ "editorSetting": {
+ "language": "python",
+ "editOnDblClick": false,
+ "completionKey": "TAB",
+ "completionSupport": true
+ },
+ "editorMode": "ace/mode/python"
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv id\u003d\u00271096\u0027
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data-root-id\u003d\"1096\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cscript
type\u003d\"application/javascript\"\u003e(function(root) {\n function
embed_document(root) {\n \n var docs_json \u003d
{\"763984d9-0529-456a-ab85-4ca811fccf92\":{\"roots\":{\"references\":[{\"a [...]
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580885519951_-291252209",
+ "id": "paragraph_1580885519951_-291252209",
+ "dateCreated": "2020-02-05 14:51:59.951",
+ "dateStarted": "2020-02-05 14:52:20.562",
+ "dateFinished": "2020-02-05 14:52:20.683",
+ "status": "FINISHED"
+ },
+ {
+ "text": "%python.ipython\n\nlayout \u003d scatter +
hv.Histogram(np.histogram(station_info[\u0027opened\u0027], bins\u003d24),
kdims\u003d[\u0027opened\u0027])\nlayout",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:52:54.836",
+ "config": {
+ "colWidth": 6.0,
+ "fontSize": 9.0,
+ "enabled": true,
+ "results": {},
+ "editorSetting": {
+ "language": "python",
+ "editOnDblClick": false,
+ "completionKey": "TAB",
+ "completionSupport": true
+ },
+ "editorMode": "ace/mode/python"
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv id\u003d\u00271419\u0027
style\u003d\u0027display: table; margin: 0 auto;\u0027\u003e\n\n\n\n\n\n
\u003cdiv class\u003d\"bk-root\"
id\u003d\"3208b87b-64b3-439e-9327-8f517a4f7a87\"
data-root-id\u003d\"1419\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cscript
type\u003d\"application/javascript\"\u003e(function(root) {\n function
embed_document(root) {\n \n var docs_json \u003d
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+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580885548751_1272982082",
+ "id": "paragraph_1580885548751_1272982082",
+ "dateCreated": "2020-02-05 14:52:28.751",
+ "dateStarted": "2020-02-05 14:52:54.884",
+ "dateFinished": "2020-02-05 14:52:55.373",
+ "status": "FINISHED"
+ },
+ {
+ "title": "HvPlot",
+ "text": "%md\n\n# HvPlot\n\n[HvPlot](https://hvplot.holoviz.org/) is a
high-level plotting API for the PyData ecosystem built on HoloViews.\n\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:11:17.248",
+ "config": {
+ "colWidth": 12.0,
+ "fontSize": 9.0,
+ "enabled": true,
+ "results": {},
+ "editorSetting": {
+ "language": "text",
+ "editOnDblClick": false,
+ "completionKey": "TAB",
+ "completionSupport": true
+ },
+ "editorMode": "ace/mode/text",
+ "title": false,
+ "editorHide": true
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
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+ "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003ch1\u003eHvPlot\u003c/h1\u003e\n\u003cp\u003e\u003ca
href\u003d\"https://hvplot.holoviz.org/\"\u003eHvPlot\u003c/a\u003e is a
high-level plotting API for the PyData ecosystem built on
HoloViews.\u003c/p\u003e\n\n\u003c/div\u003e"
+ }
+ ]
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+ "dateFinished": "2020-02-05 15:11:09.951",
+ "status": "FINISHED"
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+ {
+ "text": "%python.ipython\n\nfrom bokeh.io import output_notebook\nimport
bkzep\noutput_notebook(notebook_type\u003d\u0027zeppelin\u0027)\n\n\nimport
pandas as pd, numpy as np\n\n\nidx \u003d pd.date_range(\u00271/1/2000\u0027,
periods\u003d1000)\ndf \u003d pd.DataFrame(np.random.randn(1000, 4),
index\u003didx, columns\u003dlist(\u0027ABCD\u0027)).cumsum()\n\nimport
hvplot.pandas\ndf.hvplot()",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:55:41.519",
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type\u003d\"text/javascript\"\u003e\n(function(root) {\n function now() {\n
return new Date();\n }\n\n var force \u003d true;\n\n if (typeof
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\u003d\u003d\u003d true) { [...]
+ },
+ {
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+ "data": "\u003cdiv id\u003d\u00271667\u0027
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{\"de8c7f52-af62-4c42-b2c7-037a4ac3c5ed\":{\"roots\":{\"references\":[{\"a [...]
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+ "dateStarted": "2020-02-05 14:55:36.307",
+ "dateFinished": "2020-02-05 14:55:38.341",
+ "status": "FINISHED"
+ },
+ {
+ "text": "%python.ipython\n\nfrom hvplot.sample_data import
us_crime\n\ncolumns \u003d [\u0027Burglary rate\u0027, \u0027Larceny-theft
rate\u0027, \u0027Robbery rate\u0027, \u0027Violent Crime
rate\u0027]\n\n\nus_crime.plot.violin(y\u003dcolumns,
group_label\u003d\u0027Type of crime\u0027, value_label\u003d\u0027Rate per
100k\u0027, invert\u003dTrue)\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:56:02.776",
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+ "dateStarted": "2020-02-05 14:56:02.800",
+ "dateFinished": "2020-02-05 14:56:03.041",
+ "status": "FINISHED"
+ },
+ {
+ "text": "%python.ipython\n\nus_crime.plot.bivariate(\u0027Burglary
rate\u0027, \u0027Property crime rate\u0027, legend\u003dFalse, width\u003d500,
height\u003d400) * \\\nus_crime.plot.scatter( \u0027Burglary rate\u0027,
\u0027Property crime rate\u0027, color\u003d\u0027black\u0027, size\u003d15,
legend\u003dFalse) + \\\nus_crime.plot.table([\u0027Burglary rate\u0027,
\u0027Property crime rate\u0027], width\u003d350, height\u003d350)",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 14:56:02.798",
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+ "dateFinished": "2020-02-05 14:56:03.475",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Altair",
+ "text": "%md\n\n# Altair\n\n[Altair](https://altair-viz.github.io/) is a
declarative statistical visualization library for Python, based on Vega and
Vega-Lite, and the source is available on GitHub.\n\nWith Altair, you can spend
more time understanding your data and its meaning. Altair’s API is simple,
friendly and consistent and built on top of the powerful Vega-Lite
visualization grammar. This elegant simplicity produces beautiful and effective
visualizations with a minimal amoun [...]
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class\u003d\"markdown-body\"\u003e\n\u003ch1\u003eAltair\u003c/h1\u003e\n\u003cp\u003e\u003ca
href\u003d\"https://altair-viz.github.io/\"\u003eAltair\u003c/a\u003e is a
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Vega-Lite, and the source is available on
GitHub.\u003c/p\u003e\n\u003cp\u003eWith Altair, you can spend more time
understanding your data and its meaning. Altair’s API is simple, friendly and
consistent and built on [...]
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+ "dateStarted": "2020-02-05 15:12:10.963",
+ "dateFinished": "2020-02-05 15:12:10.975",
+ "status": "FINISHED"
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+ {
+ "text": "%python.ipython\n\nimport altair as
alt\n\nalt.renderers.enable(\u0027zeppelin\u0027)\n\n# load a simple dataset as
a pandas DataFrame\nfrom vega_datasets import data\ncars \u003d
data.cars()\n\nalt.Chart(cars).mark_point().encode(\n
x\u003d\u0027Horsepower\u0027,\n y\u003d\u0027Miles_per_Gallon\u0027,\n
color\u003d\u0027Origin\u0027,\n)",
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+ "results": {
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.vega-actions a {\n margin-right: 12px;\n color: #757575;\n
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+ "dateStarted": "2020-02-05 15:03:56.419",
+ "dateFinished": "2020-02-05 15:03:56.545",
+ "status": "FINISHED"
+ },
+ {
+ "text": "%python.ipython\n\nimport altair as alt\nimport numpy as
np\nimport pandas as pd\n\n# Compute x^2 + y^2 across a 2D grid\nx, y \u003d
np.meshgrid(range(-5, 5), range(-5, 5))\nz \u003d x ** 2 + y ** 2\n\n# Convert
this grid to columnar data expected by Altair\nsource \u003d
pd.DataFrame({\u0027x\u0027: x.ravel(),\n \u0027y\u0027:
y.ravel(),\n \u0027z\u0027:
z.ravel()})\n\nalt.Chart(source).mark_rect().encode(\n x\u003d\u0027x:O\u00
[...]
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+ "dateFinished": "2020-02-05 15:07:20.160",
+ "status": "FINISHED"
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+ "user": "anonymous",
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+}
\ No newline at end of file
diff --git a/notebook/Python Tutorial/Keras Binary Classification
(IMDB)_2F2AVWJ77.zpln b/notebook/Python Tutorial/Keras Binary Classification
(IMDB)_2F2AVWJ77.zpln
new file mode 100644
index 0000000..be88237
--- /dev/null
+++ b/notebook/Python Tutorial/Keras Binary Classification
(IMDB)_2F2AVWJ77.zpln
@@ -0,0 +1,507 @@
+{
+ "paragraphs": [
+ {
+ "text": "%md\n\nThis tutorial is from book [Deep Learning with
Python](https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438).
It use [Keras](http://keras.io/) to train binary classifier on IMDB data.\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:50:03.006",
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class\u003d\"markdown-body\"\u003e\n\u003cp\u003eThis tutorial is from book
\u003ca
href\u003d\"https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438\"\u003eDeep
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classifier on IMDB data.\u003c/p\u003e\n\n\u003c/div\u003e"
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+ "id": "paragraph_1580887284288_-572987457",
+ "dateCreated": "2020-02-05 15:21:24.288",
+ "dateStarted": "2020-02-05 15:50:03.014",
+ "dateFinished": "2020-02-05 15:50:03.025",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Load Data",
+ "text": "%python.ipython\n\n\nfrom keras.datasets import
imdb\n\n(training_data, training_labels), (testing_data, testing_labels) \u003d
imdb.load_data(num_words\u003d10000)\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:42:38.288",
+ "config": {
+ "editorSetting": {
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+ "editOnDblClick": false,
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+ "completionSupport": true
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+ "colWidth": 6.0,
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+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "Using TensorFlow backend.\n"
+ }
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+ "apps": [],
+ "progressUpdateIntervalMs": 500,
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+ "id": "20180312-171655_220733505",
+ "dateCreated": "2020-02-05 15:21:06.618",
+ "dateStarted": "2020-02-05 15:42:38.296",
+ "dateFinished": "2020-02-05 15:42:46.933",
+ "status": "FINISHED"
+ },
+ {
+ "title": "",
+ "text":
"%python.ipython\n\nprint(training_data[0])\nprint(training_labels[0])",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:50:27.642",
+ "config": {
+ "editorSetting": {
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+ "apps": [],
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+ "id": "20180312-171715_624018844",
+ "dateCreated": "2020-02-05 15:21:06.625",
+ "dateStarted": "2020-02-05 15:42:54.913",
+ "dateFinished": "2020-02-05 15:42:54.983",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Transform Data",
+ "text": "%python.ipython\n\nimport numpy as np\n\n# convert input data
to sparse matrix (each row represent one hot encoding of one doc)\ndef
vectorize_sequences(sequences, dimension \u003d 10000):\n matrix \u003d
np.zeros((len(sequences), dimension))\n for (i, sequence) in
enumerate(sequences):\n matrix[i, sequence] \u003d 1.0\n return
matrix\n\nx_train \u003d vectorize_sequences(training_data)\nx_test \u003d
vectorize_sequences(testing_data)\n\ny_train \u003d trai [...]
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:42:57.329",
+ "config": {
+ "editorSetting": {
+ "language": "python",
+ "editOnDblClick": false,
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+ "completionSupport": true
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+ "colWidth": 12.0,
+ "editorMode": "ace/mode/python",
+ "fontSize": 9.0,
+ "title": true,
+ "results": {},
+ "enabled": true
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "array([0., 1., 1., ..., 0., 0., 0.])"
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580887266625_1445359335",
+ "id": "20180312-172108_1871132312",
+ "dateCreated": "2020-02-05 15:21:06.625",
+ "dateStarted": "2020-02-05 15:42:57.337",
+ "dateFinished": "2020-02-05 15:43:01.756",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Build Model",
+ "text": "%python.ipython\n\n\nfrom keras.models import Sequential\nfrom
keras.layers import Dense\n\n\nmodel \u003d Sequential()\nmodel.add(Dense(16,
input_shape\u003d(10000,),
activation\u003d\u0027relu\u0027))\nmodel.add(Dense(16,
activation\u003d\u0027relu\u0027))\nmodel.add(Dense(1,
activation\u003d\u0027sigmoid\u0027))\n\nmodel.compile(loss\u003d\u0027binary_crossentropy\u0027,
optimizer\u003d\u0027rmsprop\u0027, metrics\u003d[\u0027accuracy\u0027])\n\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:43:03.522",
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+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "WARNING: Logging before flag parsing goes to
stderr.\nW0205 15:43:03.542002 4550231488 deprecation_wrapper.py:119] From
/Users/jzhang/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:74:
The name tf.get_default_graph is deprecated. Please use
tf.compat.v1.get_default_graph instead.\n\nW0205 15:43:03.574465 4550231488
deprecation_wrapper.py:119] From
/Users/jzhang/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:517:
T [...]
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
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+ "dateStarted": "2020-02-05 15:43:03.529",
+ "dateFinished": "2020-02-05 15:43:03.755",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Traing Model",
+ "text": "%python.ipython\n\nx_val \u003d x_train[:10000]\ny_val \u003d
y_train[:10000]\npartial_x_train \u003d x_train[10000:]\npartial_y_train \u003d
y_train[10000:]\n\nhistory \u003d model.fit(partial_x_train, partial_y_train,
epochs\u003d20, batch_size\u003d512, validation_data\u003d(x_val, y_val))\n\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:43:07.601",
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+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "W0205 15:43:08.120182 4550231488
deprecation_wrapper.py:119] From
/Users/jzhang/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:986:
The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add
instead.\n\nTrain on 15000 samples, validate on 10000 samples\nEpoch 1/20\n\r
512/15000 [\u003e.............................] - ETA: 14s - loss: 0.6939 -
acc:
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[...]
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+ "dateFinished": "2020-02-05 15:43:41.441",
+ "status": "FINISHED"
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+ {
+ "title": "",
+ "text": "%python.ipython\n\nhistory_dict \u003d
history.history\nhistory_dict",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:43:32.021",
+ "config": {
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+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "{\u0027val_loss\u0027: [0.37968397402763365,\n
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0.2841434906005859,\n 0.3164743734836578,\n 0.312709903717041,\n
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+ "id": "20180312-181145_1796362449",
+ "dateCreated": "2020-02-05 15:21:06.629",
+ "dateStarted": "2020-02-05 15:43:32.029",
+ "dateFinished": "2020-02-05 15:43:41.513",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Initialize Bokeh for training process plotting",
+ "text": "%python.ipython\n\nfrom bokeh.io import output_notebook,
show\nfrom bokeh.plotting import figure\n\nimport
bkzep\n\noutput_notebook(notebook_type\u003d\u0027zeppelin\u0027)\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:43:45.275",
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+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
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\u003cspan id\u003d\"1001\"\u003eLoading BokehJS ...\u003c/span\u003e\n
\u003c/div\u003e\n\n"
+ },
+ {
+ "type": "HTML",
+ "data": "\u003cscript
type\u003d\"text/javascript\"\u003e\n(function(root) {\n function now() {\n
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root._bokeh_is_loading \u003d undefined;\n }\n\n \n\n \n if (typeof
(root._bokeh_timeout) \u003d\u003d\u003d \"undefined\" || force
\u003d\u003d\u003d true) { [...]
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580887266629_755430293",
+ "id": "20180312-212623_945231959",
+ "dateCreated": "2020-02-05 15:21:06.629",
+ "dateStarted": "2020-02-05 15:43:45.294",
+ "dateFinished": "2020-02-05 15:43:45.783",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Accuracy Plotting",
+ "text": "%python.ipython\n\nfrom bokeh.io import show\nfrom
bokeh.plotting import figure\n\nval_acc, acc \u003d
history_dict[\u0027val_acc\u0027], history_dict[\u0027acc\u0027]\n\nfig \u003d
figure()\nfig.line(range(1, len(val_acc) + 1), val_acc,
color\u003d\u0027red\u0027, legend\u003d\u0027Validation
Accuracy\u0027)\nfig.line(range(1, len(acc) +1), acc,
color\u003d\u0027blue\u0027, legend\u003d\u0027Training
Accuracy\u0027)\nfig.xaxis.axis_label \u003d \u0027Epochs\u0027\nfig.yax [...]
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:43:47.556",
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+ "colWidth": 6.0,
+ "editorMode": "ace/mode/python",
+ "fontSize": 9.0,
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+ "results": {},
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+ },
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+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\n\n\n\n\n\n \u003cdiv class\u003d\"bk-root\"
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+ },
+ {
+ "type": "HTML",
+ "data": "\u003cscript
type\u003d\"text/javascript\"\u003e(function(root) {\n function
embed_document(root) {\n \n var docs_json \u003d
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+ }
+ ]
+ },
+ "apps": [],
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+ "dateStarted": "2020-02-05 15:43:47.564",
+ "dateFinished": "2020-02-05 15:43:47.681",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Loss Plotting",
+ "text": "%python.ipython\n\nfrom bokeh.io import show\nfrom
bokeh.plotting import figure\n\nval_loss, loss \u003d
history_dict[\u0027val_loss\u0027], history_dict[\u0027loss\u0027]\n\nfig
\u003d figure()\nfig.line(range(1, len(val_loss) + 1), val_loss,
color\u003d\u0027red\u0027, legend\u003d\u0027Validation
Loss\u0027)\nfig.line(range(1, len(loss) +1), loss,
color\u003d\u0027blue\u0027, legend\u003d\u0027Training
Loss\u0027)\n\nfig.xaxis.axis_label \u003d \u0027Epochs\u0027\nfig.y [...]
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:43:49.169",
+ "config": {
+ "editorSetting": {
+ "language": "python",
+ "editOnDblClick": false,
+ "completionKey": "TAB",
+ "completionSupport": true
+ },
+ "colWidth": 6.0,
+ "editorMode": "ace/mode/python",
+ "fontSize": 9.0,
+ "title": true,
+ "results": {},
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+ {
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+ },
+ {
+ "type": "HTML",
+ "data": "\u003cscript
type\u003d\"text/javascript\"\u003e(function(root) {\n function
embed_document(root) {\n \n var docs_json \u003d
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[...]
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580887266630_-932015545",
+ "id": "20180312-212825_683262480",
+ "dateCreated": "2020-02-05 15:21:06.630",
+ "dateStarted": "2020-02-05 15:43:49.182",
+ "dateFinished": "2020-02-05 15:43:49.332",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Retrain the model in 4 epochs",
+ "text": "%python.ipython\n\nmodel.fit(x_train, y_train, epochs\u003d4,
batch_size\u003d512)\nresults \u003d model.evaluate(x_test,
y_test)\nprint(results)\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:43:52.363",
+ "config": {
+ "editorSetting": {
+ "language": "python",
+ "editOnDblClick": false,
+ "completionKey": "TAB",
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+ "results": {},
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+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "Epoch 1/4\n\r 512/25000 [..............................]
- ETA: 1s - loss: 0.2078 - acc:
0.9590\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r
1536/25000 [\u003e.............................] - ETA: 1s - loss: 0.2439 -
acc:
0.9564\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\
[...]
+ }
+ ]
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580887266631_-208421764",
+ "id": "20180312-213402_1553086977",
+ "dateCreated": "2020-02-05 15:21:06.631",
+ "dateStarted": "2020-02-05 15:43:52.369",
+ "dateFinished": "2020-02-05 15:44:02.728",
+ "status": "FINISHED"
+ },
+ {
+ "title": "",
+ "text": "%python.ipython\n",
+ "user": "anonymous",
+ "dateUpdated": "2020-02-05 15:21:06.633",
+ "config": {
+ "editorSetting": {
+ "language": "python",
+ "editOnDblClick": false,
+ "completionKey": "TAB"
+ },
+ "colWidth": 12.0,
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+ "fontSize": 9.0,
+ "results": {},
+ "enabled": true
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1580887266633_-1827767869",
+ "id": "20180312-214821_1740662910",
+ "dateCreated": "2020-02-05 15:21:06.633",
+ "status": "READY"
+ }
+ ],
+ "name": "Keras Binary Classification (IMDB)",
+ "id": "2F2AVWJ77",
+ "defaultInterpreterGroup": "spark",
+ "version": "0.9.0-SNAPSHOT",
+ "permissions": {},
+ "noteParams": {},
+ "noteForms": {},
+ "angularObjects": {},
+ "config": {
+ "isZeppelinNotebookCronEnable": true
+ },
+ "info": {},
+ "path": "/Python Tutorial/Keras Binary Classification (IMDB)"
+}
\ No newline at end of file
diff --git a/notebook/Zeppelin Tutorial/Matplotlib (Python,
PySpark)_2C2AUG798.zpln b/notebook/Python Tutorial/Matplotlib (Python,
PySpark)_2C2AUG798.zpln
similarity index 96%
rename from notebook/Zeppelin Tutorial/Matplotlib (Python,
PySpark)_2C2AUG798.zpln
rename to notebook/Python Tutorial/Matplotlib (Python, PySpark)_2C2AUG798.zpln
index bbf25bf..6a7319a 100644
--- a/notebook/Zeppelin Tutorial/Matplotlib (Python, PySpark)_2C2AUG798.zpln
+++ b/notebook/Python Tutorial/Matplotlib (Python, PySpark)_2C2AUG798.zpln
@@ -1,9 +1,9 @@
{
"paragraphs": [
{
- "text": "%md\n## Introduction\nIn this tutorial we will go through some
of the basic features of Zeppelin\u0027s built-in matplotlib integration.
\n\n### Prerequisites\n`matplotlib` must be installed to your local python
installation. (use `pip install matplotlib` or `conda install matplotlib` if
you have `conda`). Additionally, you will need Zeppelin\u0027s matplotlib
backend files which are usually found in `$ZEPPELIN_HOME/lib/python`. Although
Zeppelin should automatically find [...]
+ "text": "%md\n## Introduction\nIn this tutorial we will go through some
of the basic features of Zeppelin\u0027s built-in matplotlib integration. To be
noticed, this tutorial is only for Vanilla Python Interpreter (`%python` or
`%spark.pyspark`). You can refer another tutorial (IPython Visualization
Tutorial) for using Matplotlib in IPython Interpreter.\n\n###
Prerequisites\n`matplotlib` must be installed to your local python
installation. (use `pip install matplotlib` or `conda in [...]
"user": "anonymous",
- "dateUpdated": "Dec 17, 2016 3:33:25 PM",
+ "dateUpdated": "2020-02-05 15:48:09.193",
"config": {
"tableHide": false,
"colWidth": 12.0,
@@ -26,33 +26,34 @@
"editorSetting": {
"language": "text",
"editOnDblClick": false
- }
+ },
+ "fontSize": 9.0
},
"settings": {
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627954_-1473548609",
- "id": "20160614-174657_1772993700",
"results": {
"code": "SUCCESS",
"msg": [
{
"type": "HTML",
- "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003ch2\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eIn
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Zeppelin\u0026rsquo;s built-in matplotlib integration.
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must be installed to your local python installation. (use \u003ccode\u003epip
install matplotlib\u003c/code\u003e or \u003ccode\u00 [...]
+ "data": "\u003cdiv
class\u003d\"markdown-body\"\u003e\n\u003ch2\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eIn
this tutorial we will go through some of the basic features of
Zeppelin\u0026rsquo;s built-in matplotlib integration. To be noticed, this
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(\u003ccode\u003e%python\u003c/code\u003e or
\u003ccode\u003e%spark.pyspark\u003c/code\u003e). You can refer another
tutorial (IPython Visualization Tutorial) for using Matplotlib [...]
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
- "dateStarted": "Dec 17, 2016 3:33:25 PM",
- "dateFinished": "Dec 17, 2016 3:33:25 PM",
- "status": "FINISHED",
- "progressUpdateIntervalMs": 500
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627954_-1473548609",
+ "id": "20160614-174657_1772993700",
+ "dateCreated": "2016-11-02 14:53:47.000",
+ "dateStarted": "2020-02-05 15:47:58.416",
+ "dateFinished": "2020-02-05 15:47:59.573",
+ "status": "FINISHED"
},
{
"text": "%python\nimport matplotlib.pyplot as plt\nplt.plot([1, 2, 3])",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 12.0,
"editorMode": "ace/mode/python",
@@ -75,9 +76,6 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627958_-1475087605",
- "id": "20161101-192232_289486976",
"results": {
"code": "SUCCESS",
"msg": [
@@ -87,14 +85,17 @@
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627958_-1475087605",
+ "id": "20161101-192232_289486976",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"text": "%md\nNotice how an explicit call to `show()` is not necessary.
This is accomplished via a post-execute hook which tells Zeppelin to plot all
currently open matplotlib figures after executing the rest of the
paragraph.\n### Plotting multiple figures\nWe can easily plot multiple figures
at once too:",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 12.0,
"editorMode": "ace/mode/markdown",
@@ -118,9 +119,6 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627958_-1475087605",
- "id": "20160617-002131_1552178409",
"results": {
"code": "SUCCESS",
"msg": [
@@ -130,14 +128,17 @@
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627958_-1475087605",
+ "id": "20160617-002131_1552178409",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"text": "%python\n# Figure 1\nplt.plot([1, 2, 3])\n\n# Figure
2\nplt.figure()\nplt.plot([3, 2, 1])",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 12.0,
"editorMode": "ace/mode/python",
@@ -160,9 +161,6 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627959_-1475472354",
- "id": "20161101-193533_2096366908",
"results": {
"code": "SUCCESS",
"msg": [
@@ -172,14 +170,17 @@
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627959_-1475472354",
+ "id": "20161101-193533_2096366908",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"text": "%md\n### Changing the default inline plotting behavior\nBoth
the `python` and `pyspark` include a built-in function for changing some
default inline plotting behavior. For example, we can change the default size
of each figure in pixels to 400x300 in svg format using: ",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"tableHide": false,
"colWidth": 12.0,
@@ -204,9 +205,6 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627959_-1475472354",
- "id": "20160614-174421_274483707",
"results": {
"code": "SUCCESS",
"msg": [
@@ -216,14 +214,17 @@
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627959_-1475472354",
+ "id": "20160614-174421_274483707",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"text": "%python\nz.configure_mpl(width\u003d400, height\u003d300,
fmt\u003d\u0027svg\u0027)\nplt.plot([1, 2, 3])",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 12.0,
"editorMode": "ace/mode/scala",
@@ -248,9 +249,6 @@
},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627959_-1475472354",
- "id": "20160616-234947_579056637",
"results": {
"code": "SUCCESS",
"msg": [
@@ -260,14 +258,17 @@
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627959_-1475472354",
+ "id": "20160616-234947_579056637",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"text": "%md\n### Iteratively updating a plot\n#### (a) Using multiple
plots\nNow let\u0027s show an example where we update each element of the plot
in a separate paragraph. However, you may have noticed that each matplotlib
figure instance gets closed immediately after its shown. To fix this, we set
the `close` property to `False` in our configuration:",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 12.0,
"editorMode": "ace/mode/markdown",
@@ -291,9 +292,6 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627960_-1477396098",
- "id": "20160617-140439_1111727405",
"results": {
"code": "SUCCESS",
"msg": [
@@ -303,15 +301,18 @@
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627960_-1477396098",
+ "id": "20160617-140439_1111727405",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"title": "First line",
"text": "%python\nplt.close() # Added here to reset the first plot when
rerunning the paragraph\nz.configure_mpl(width\u003d600, height\u003d400,
fmt\u003d\u0027png\u0027, close\u003dFalse)\nplt.plot([1, 2, 3],
label\u003dr\u0027$y\u003dx$\u0027)",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 12.0,
"title": true,
@@ -334,9 +335,6 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627960_-1477396098",
- "id": "20161101-195657_1336292109",
"results": {
"code": "SUCCESS",
"msg": [
@@ -346,15 +344,18 @@
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627960_-1477396098",
+ "id": "20161101-195657_1336292109",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"title": "Second line",
"text": "%python\nplt.plot([3, 2, 1],
label\u003dr\u0027$y\u003d3-x$\u0027)",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 12.0,
"editorMode": "ace/mode/python",
@@ -378,9 +379,6 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627961_-1477780847",
- "id": "20161101-195937_907325325",
"results": {
"code": "SUCCESS",
"msg": [
@@ -390,15 +388,18 @@
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627961_-1477780847",
+ "id": "20161101-195937_907325325",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"title": "Label axes",
"text": "%python\nplt.xlabel(r\u0027$x$\u0027,
fontsize\u003d20)\nplt.ylabel(r\u0027$y$\u0027, fontsize\u003d20)",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 12.0,
"editorMode": "ace/mode/python",
@@ -422,9 +423,6 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627962_-1476626600",
- "id": "20161101-200014_2113468597",
"results": {
"code": "SUCCESS",
"msg": [
@@ -434,15 +432,18 @@
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627962_-1476626600",
+ "id": "20161101-200014_2113468597",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"title": "Add legend",
"text": "%python\nplt.legend(loc\u003d\u0027upper center\u0027,
fontsize\u003d20)",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 12.0,
"editorMode": "ace/mode/python",
@@ -466,9 +467,6 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627962_-1476626600",
- "id": "20161101-200141_1493024813",
"results": {
"code": "SUCCESS",
"msg": [
@@ -478,15 +476,18 @@
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627962_-1476626600",
+ "id": "20161101-200141_1493024813",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"title": "Add title",
"text": "%python\nplt.title(\u0027Inline plotting example\u0027,
fontsize\u003d20)",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 12.0,
"editorMode": "ace/mode/python",
@@ -510,9 +511,6 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627963_-1477011349",
- "id": "20161101-200445_78775142",
"results": {
"code": "SUCCESS",
"msg": [
@@ -522,14 +520,17 @@
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627963_-1477011349",
+ "id": "20161101-200445_78775142",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"text": "%md\n####(b) Using a single plot\nTo iteratively update a
single plot, we can leverage Zeppelin\u0027s built-in Angular Display System.
Currently this feature is only available for the `pyspark` interpreter for
raster (png and jpg) formats. To enable this, we must set a special `angular`
flag to `True` in our configuration:",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 12.0,
"editorMode": "ace/mode/markdown",
@@ -553,9 +554,6 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627963_-1477011349",
- "id": "20161101-200541_1283841564",
"results": {
"code": "SUCCESS",
"msg": [
@@ -565,16 +563,19 @@
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627963_-1477011349",
+ "id": "20161101-200541_1283841564",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"title": "First line (figure will be displayed here)",
"text": "%pyspark\nimport matplotlib.pyplot as plt\nplt.close() # Added
here to reset the plot when rerunning the
paragraph\nz.configure_mpl(angular\u003dTrue, close\u003dFalse)\nplt.plot([1,
2, 3], label\u003dr\u0027$y\u003dx$\u0027)",
"user": "anonymous",
- "dateUpdated": "Nov 2, 2016 2:55:37 PM",
+ "dateUpdated": "2016-11-02 14:55:37.000",
"config": {
"colWidth": 7.0,
"editorMode": "ace/mode/python",
@@ -598,9 +599,6 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627963_-1477011349",
- "id": "20161101-200754_739212093",
"results": {
"code": "SUCCESS",
"msg": [
@@ -610,16 +608,19 @@
}
]
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
- "dateStarted": "Nov 2, 2016 2:54:53 PM",
- "dateFinished": "Nov 2, 2016 2:55:04 PM",
- "status": "FINISHED",
- "progressUpdateIntervalMs": 500
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627963_-1477011349",
+ "id": "20161101-200754_739212093",
+ "dateCreated": "2016-11-02 14:53:47.000",
+ "dateStarted": "2016-11-02 14:54:53.000",
+ "dateFinished": "2016-11-02 14:55:04.000",
+ "status": "FINISHED"
},
{
"title": "Second line",
"text": "%pyspark\nplt.plot([3, 2, 1],
label\u003dr\u0027$y\u003d3-x$\u0027)",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 5.0,
"title": true,
@@ -630,22 +631,22 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627964_-1478935094",
- "id": "20161101-200854_1676504884",
"results": {
"code": "SUCCESS",
"msg": []
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627964_-1478935094",
+ "id": "20161101-200854_1676504884",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"title": "Label axes",
"text": "%pyspark\nplt.xlabel(r\u0027$x$\u0027,
fontsize\u003d20)\nplt.ylabel(r\u0027$y$\u0027, fontsize\u003d20)",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 5.0,
"title": true,
@@ -656,22 +657,22 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627964_-1478935094",
- "id": "20161101-200851_314384892",
"results": {
"code": "SUCCESS",
"msg": []
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627964_-1478935094",
+ "id": "20161101-200851_314384892",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"title": "Add legend",
"text": "%pyspark\nplt.legend(loc\u003d\u0027upper center\u0027,
fontsize\u003d20)",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 5.0,
"editorMode": "ace/mode/python",
@@ -683,22 +684,22 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627964_-1478935094",
- "id": "20161101-201552_651686132",
"results": {
"code": "SUCCESS",
"msg": []
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627964_-1478935094",
+ "id": "20161101-201552_651686132",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"title": "Add title",
"text": "%pyspark\nplt.title(\u0027Inline plotting example\u0027,
fontsize\u003d20)",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 5.0,
"editorMode": "ace/mode/python",
@@ -710,21 +711,21 @@
"params": {},
"forms": {}
},
- "apps": [],
- "jobName": "paragraph_1478123627965_-1479319843",
- "id": "20161101-202024_1645454710",
"results": {
"code": "SUCCESS",
"msg": []
},
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "apps": [],
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1478123627965_-1479319843",
+ "id": "20161101-202024_1645454710",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
},
{
"text": "",
- "dateUpdated": "Nov 2, 2016 2:53:47 PM",
+ "dateUpdated": "2016-11-02 14:53:47.000",
"config": {
"colWidth": 12.0,
"graph": {
@@ -743,16 +744,20 @@
"forms": {}
},
"apps": [],
+ "progressUpdateIntervalMs": 500,
"jobName": "paragraph_1478123627965_-1479319843",
"id": "20161102-124716_1703649609",
- "dateCreated": "Nov 2, 2016 2:53:47 PM",
+ "dateCreated": "2016-11-02 14:53:47.000",
"status": "READY",
- "errorMessage": "",
- "progressUpdateIntervalMs": 500
+ "errorMessage": ""
}
],
"name": "Matplotlib (Python, PySpark)",
"id": "2C2AUG798",
+ "defaultInterpreterGroup": "spark",
+ "permissions": {},
+ "noteParams": {},
+ "noteForms": {},
"angularObjects": {
"2C6WUGPNH:shared_process": [],
"2C4A8RJNB:shared_process": [],
@@ -774,6 +779,9 @@
"2C49A6WY3:shared_process": [],
"2C3NE73HG:shared_process": []
},
- "config": {},
- "info": {}
-}
+ "config": {
+ "isZeppelinNotebookCronEnable": true
+ },
+ "info": {},
+ "path": "/Python Tutorial/Matplotlib (Python, PySpark)"
+}
\ No newline at end of file
