Python 2 & 3 Interpreter for Apache Zeppelin

Overview

Zeppelin supports python language which is very popular in data analytics and machine learning.

Main Features

Play Python in Zeppelin docker

For beginner, we would suggest you to play Python in Zeppelin docker first. In the Zeppelin docker image, we have already installed miniconda and lots of useful python libraries including IPython's prerequisites, so %python would use IPython.

Without any extra configuration, you can run most of tutorial notes under folder Python Tutorial directly.

docker run -u $(id -u) -p 8080:8080 --rm --name zeppelin apache/zeppelin:0.10.0

After running the above command, you can open http://localhost:8080 to play Python in Zeppelin.

Configuration

Vanilla Python Interpreter (%python)

The vanilla python interpreter provides basic python interpreter feature, only python installed is required.

Matplotlib integration

The vanilla python interpreter can display matplotlib figures inline automatically using the matplotlib:

%python

import matplotlib.pyplot as plt
plt.plot([1, 2, 3])

The output of this command will by default be converted to HTML by implicitly making use of the %html magic. Additional configuration can be achieved using the builtin z.configure_mpl() method. For example,

z.configure_mpl(width=400, height=300, fmt='svg')
plt.plot([1, 2, 3])

Will produce a 400x300 image in SVG format, which by default are normally 600x400 and PNG respectively. In the future, another option called angular can be used to make it possible to update a plot produced from one paragraph directly from another (the output will be %angular instead of %html). However, this feature is already available in the pyspark interpreter. More details can be found in the included "Zeppelin Tutorial: Python - matplotlib basic" tutorial notebook.

If Zeppelin cannot find the matplotlib backend files (which should usually be found in $ZEPPELIN_HOME/interpreter/lib/python) in your PYTHONPATH, then the backend will automatically be set to agg, and the (otherwise deprecated) instructions below can be used for more limited inline plotting.

If you are unable to load the inline backend, use z.show(plt):

%python

import matplotlib.pyplot as plt
plt.figure()
(.. ..)
z.show(plt)
plt.close()

The z.show() function can take optional parameters to adapt graph dimensions (width and height) as well as output format (png or optionally svg).

%python

z.show(plt, width='50px')
z.show(plt, height='150px', fmt='svg')

IPython Interpreter (%python.ipython) (recommended)

IPython is more powerful than the vanilla python interpreter with extra functionality. This is what we recommend you to use instead of vanilla python interpreter. You can use IPython with Python2 or Python3 which depends on which python you set in zeppelin.python.

Prerequisites

• For non-anaconda environment, You need to install the following packages

pip install jupyter
pip install grpcio
pip install protobuf

• For anaconda environment (zeppelin.python points to the python under anaconda)

pip install grpcio
pip install protobuf

Zeppelin will check the above prerequisites when using %python, if IPython prerequisites are met, %python would use IPython interpreter, otherwise it would use vanilla Python interpreter in %python.

In addition to all the basic functions of the vanilla python interpreter, you can use all the IPython advanced features as you use it in Jupyter Notebook. Take a look at tutorial note Python Tutorial/1. IPython Basic and Python Tutorial/2. IPython Visualization Tutorial for how to use IPython in Zeppelin.

Use IPython magic

%python.ipython

#python help
range?

#timeit
%timeit range(100)

Use matplotlib

%python.ipython

%matplotlib inline
import matplotlib.pyplot as plt

print("hello world")
data=[1,2,3,4]
plt.figure()
plt.plot(data)

Run shell command

%python.ipython

!pip install pandas

Colored text output

More types of visualization

e.g. You can use hvplot in the same way as in Jupyter, Take a look at tutorial note Python Tutorial/2. IPython Visualization Tutorial for more visualization examples.

Better code completion

Type tab can give you all the completion candidates just like in Jupyter.

Pandas Integration

Apache Zeppelin Table Display System provides built-in data visualization capabilities. Python interpreter leverages it to visualize Pandas DataFrames via z.show() API.

For example:

By default, z.show only display 1000 rows, you can configure zeppelin.python.maxResult to adjust the max number of rows.

SQL over Pandas DataFrames

There is a convenience %python.sql interpreter that matches Apache Spark experience in Zeppelin and enables usage of SQL language to query Pandas DataFrames and visualization of results through built-in Table Display System. %python.sql can access dataframes defined in %python.

Prerequisites

• Pandas pip install pandas
• PandaSQL pip install -U pandasql

Here's one example:

• first paragraph

  %python
  import pandas as pd
  rates = pd.read_csv("bank.csv", sep=";")
  

• next paragraph

  %python.sql
  SELECT * FROM rates WHERE age < 40
  

Using Zeppelin Dynamic Forms

You can leverage Zeppelin Dynamic Form inside your Python code.

Example :

%python

### Input form
print(z.input("f1","defaultValue"))

### Select form
print(z.select("f2",[("o1","1"),("o2","2")],"o1"))

### Checkbox form
print("".join(z.checkbox("f3", [("o1","1"), ("o2","2")],["o1"])))

ZeppelinContext API

Python interpreter create a variable z which represent ZeppelinContext for you. User can use it to do more fancy and complex things in Zeppelin.

Run Python interpreter in yarn cluster

Zeppelin supports to run interpreter in yarn cluster which means the python interpreter can run in a yarn container. This can achieve better multi-tenant for python interpreter especially when you already have a hadoop yarn cluster.

But there's one critical problem to run python in yarn cluster: how to manage the python environment in yarn container. Because hadoop yarn cluster is a distributed cluster environment which is composed of many nodes, and your python interpreter can start in any node. It is not practical to manage python environment in each node beforehand.

So in order to run python in yarn cluster, we would suggest you to use conda to manage your python environment, and Zeppelin can ship your conda environment to yarn container, so that each python interpreter can have its own python environment without affecting each other.

Python interpreter in yarn cluster only works for IPython, so make sure IPython's prerequisites are met. So make sure including the following packages in Step 1.

• python
• jupyter
• grpcio
• protobuf

Step 1

We would suggest you to use conda-pack to create archive of conda environment, and ship it to yarn container. Otherwise python interpreter will use the python executable file in PATH of yarn container.

Here's one example of yaml file which could be used to create a conda environment with python 3 and some useful python libraries.

• Create yaml file for conda environment, write the following content into file python_3_env.yml

name: python_3_env
channels:
  - conda-forge
  - defaults
dependencies:
  - python=3.7 
  - jupyter
  - grpcio
  - protobuf
  - pycodestyle
  - numpy
  - pandas
  - scipy
  - pandasql  
  - panel
  - pyyaml
  - seaborn
  - plotnine
  - hvplot
  - intake
  - intake-parquet
  - intake-xarray
  - altair
  - vega_datasets
  - pyarrow

• Create conda environment via this yml file using either conda or mamba

conda env create -f python_3_env.yml

mamba env create -f python_3_env

• Pack the conda environment using conda

conda pack -n python_3_env

Step 2

Specify the following properties to enable yarn mode for python interpreter.

%python.conf

zeppelin.interpreter.launcher yarn
zeppelin.yarn.dist.archives /home/hadoop/python_3_env.tar.gz#environment
zeppelin.interpreter.conda.env.name environment

Setting zeppelin.interpreter.launcher as yarn will launch python interpreter in yarn cluster.

zeppelin.yarn.dist.archives is the python conda environment tar which is created in step 1. This tar will be shipped to yarn container and untar in the working directory of yarn container. environment in /home/hadoop/python_3.tar.gz#environment is the folder name after untar.

This folder name should be the same as zeppelin.interpreter.conda.env.name. Usually we name it as environment here.

Python environments (used for vanilla python interpreter in non-yarn mode)

Default

By default, PythonInterpreter will use python command defined in zeppelin.python property to run python process. The interpreter can use all modules already installed (with pip, easy_install...)

Conda

Conda is an package management system and environment management system for python. %python.conda interpreter lets you change between environments.

Usage

• get the Conda Information:

  %python.conda info
  

• list the Conda environments:

  %python.conda env list
  

• create a conda enviornment:

  %python.conda create --name [ENV NAME]
  

• activate an environment (python interpreter will be restarted):

  %python.conda activate [ENV NAME]
  

• deactivate

  %python.conda deactivate
  

• get installed package list inside the current environment

  %python.conda list
  

• install package

  %python.conda install [PACKAGE NAME]
  

• uninstall package

  %python.conda uninstall [PACKAGE NAME]
  

Docker

%python.docker interpreter allows PythonInterpreter creates python process in a specified docker container.

Usage

• activate an environment

  %python.docker activate [Repository]
  %python.docker activate [Repository:Tag]
  %python.docker activate [Image Id]
  

• deactivate

  %python.docker deactivate
  

Here is an example

# activate latest tensorflow image as a python environment
%python.docker activate gcr.io/tensorflow/tensorflow:latest

Community

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