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Python Data Science Module Package

Greenplum Database provides a collection of data science-related Python modules that can be used with the Greenplum Database PL/Python language. You can download these modules in .gppkg format from Pivotal Network.

This section contains the following information:

Python Data Science Modules
Installing the Python Data Science Module Package
Uninstalling the Python Data Science Module Package

For information about the Greenplum Database PL/Python Language, see Greenplum PL/Python Language Extension.

Python Data Science Modules

Modules provided in the Python Data Science package include:

Table 1. Data Science Modules
Module Name	Description/Used For
atomicwrites	Atomic file writes
attrs	Declarative approach for defining class attributes
Autograd	Gradient-based optimization
backports.functools-lru-cache	Backports `functools.lru_cache` from Python 3.3
Beautiful Soup	Navigating HTML and XML
Blis	Blis linear algebra routines
Boto	Amazon Web Services library
Boto3	The AWS SDK
botocore	Low-level, data-driven core of boto3
Bottleneck	Fast NumPy array functions
Bz2file	Read and write bzip2-compressed files
Certifi	Provides Mozilla CA bundle
Chardet	Universal encoding detector for Python 2 and 3
ConfigParser	Updated `configparser` module
contextlib2	Backports and enhancements for the `contextlib` module
Cycler	Composable style cycles
cymem	Manage calls to calloc/free through Cython
Docutils	Python documentation utilities
enum34	Backport of Python 3.4 Enum
Funcsigs	Python function signatures from PEP362
functools32	Backport of the `functools` module from Python 3.2.3
funcy	Functional tools focused on practicality
future	Compatibility layer between Python 2 and Python 3
futures	Backport of the `concurrent.futures` package from Python 3
Gensim	Topic modeling and document indexing
GluonTS (Python 3 only)	Probabilistic time series modeling
h5py	Read and write HDF5 files
idna	Internationalized Domain Names in Applications (IDNA)
importlib-metadata	Read metadata from Python packages
Jinja2	Stand-alone template engine
JMESPath	JSON Matching Expressions
Joblib	Python functions as pipeline jobs
jsonschema	JSON Schema validation
Keras (RHEL/CentOS 7 only)	Deep learning
Keras Applications	Reference implementations of popular deep learning models
Keras Preprocessing	Easy data preprocessing and data augmentation for deep learning models
Kiwi	A fast implementation of the Cassowary constraint solver
Lifelines	Survival analysis
lxml	XML and HTML processing
MarkupSafe	Safely add untrusted strings to HTML/XML markup
Matplotlib	Python plotting package
mock	Rolling backport of `unittest.mock`
more-itertools	More routines for operating on iterables, beyond itertools
MurmurHash	Cython bindings for MurmurHash
NLTK	Natural language toolkit
NumExpr	Fast numerical expression evaluator for NumPy
NumPy	Scientific computing
packaging	Core utilities for Python packages
Pandas	Data analysis
pathlib, pathlib2	Object-oriented filesystem paths
patsy	Package for describing statistical models and for building design matrices
Pattern-en	Part-of-speech tagging
pip	Tool for installing Python packages
plac	Command line arguments parser
pluggy	Plugin and hook calling mechanisms
preshed	Cython hash table that trusts the keys are pre-hashed
protobuf	Protocol buffers
py	Cross-python path, ini-parsing, io, code, log facilities
pyLDAvis	Interactive topic model visualization
PyMC3	Statistical modeling and probabilistic machine learning
pyparsing	Python parsing
pytest	Testing framework
python-dateutil	Extensions to the standard Python datetime module
pytz	World timezone definitions, modern and historical
PyYAML	YAML parser and emitter
requests	HTTP library
s3transfer	Amazon S3 transfer manager
scandir	Directory iteration function
scikit-learn	Machine learning data mining and analysis
SciPy	Scientific computing
setuptools	Download, build, install, upgrade, and uninstall Python packages
six	Python 2 and 3 compatibility library
smart-open	Utilities for streaming large files (S3, HDFS, gzip, bz2, and so forth)
spaCy	Large scale natural language processing
srsly	Modern high-performance serialization utilities for Python
StatsModels	Statistical modeling
subprocess32	Backport of the subprocess module from Python 3
Tensorflow (RHEL/CentOS 7 only)	Numerical computation using data flow graphs
Theano	Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs
thinc	Practical Machine Learning for NLP
tqdm	Fast, extensible progress meter
urllib3	HTTP library with thread-safe connection pooling, file post, and more
wasabi	Lightweight console printing and formatting toolkit
wcwidth	Measures number of Terminal column cells of wide-character codes
Werkzeug	Comprehensive WSGI web application library
wheel	A built-package format for Python
XGBoost	Gradient boosting, classifying, ranking
zipp	Backport of pathlib-compatible object wrapper for zip files

Installing the Python Data Science Module Package

Before you install the Python Data Science Module package, make sure that your Greenplum Database is running, you have sourced greenplum_path.sh, and that the $MASTER_DATA_DIRECTORY and $GPHOME environment variables are set.

Note: The PyMC3 module depends on Tk. If you want to use PyMC3, you must install the tk OS package on every node in your cluster. For example:

$ yum install tk

Locate the Python Data Science module package that you built or downloaded.
The file name format of the package is DataSciencePython-<version>-relhel<N>-x86_64.gppkg.
Copy the package to the Greenplum Database master host.
Use the gppkg command to install the package. For example:
```
$ gppkg -i DataSciencePython-<version>-relhel<N>_x86_64.gppkg
```
gppkg installs the Python Data Science modules on all nodes in your Greenplum Database cluster. The command also updates the PYTHONPATH, PATH, and LD_LIBRARY_PATH environment variables in your greenplum_path.sh file.
Restart Greenplum Database. You must re-source greenplum_path.sh before restarting your Greenplum cluster:
```
$ source /usr/local/greenplum-db/greenplum_path.sh
$ gpstop -r
```

The Greenplum Database Python Data Science Modules are installed in the following directory:

$GPHOME/ext/DataSciencePython/lib/python2.7/site-packages/

Uninstalling the Python Data Science Module Package

Use the gppkg utility to uninstall the Python Data Science Module package. You must include the version number in the package name you provide to gppkg.

To determine your Python Data Science Module package version number and remove this package:

$ gppkg -q --all | grep DataSciencePython
DataSciencePython-<version>
$ gppkg -r DataSciencePython-<version>

The command removes the Python Data Science modules from your Greenplum Database cluster. It also updates the PYTHONPATH, PATH, and LD_LIBRARY_PATH environment variables in your greenplum_path.sh file to their pre-installation values.

Re-source greenplum_path.sh and restart Greenplum Database after you remove the Python Data Science Module package:

$ . /usr/local/greenplum-db/greenplum_path.sh
$ gpstop -r

Note: When you uninstall the Python Data Science Module package from your Greenplum Database cluster, any UDFs that you have created that import Python modules installed with this package will return an error.