Wheelodex » Projects » nzpyida

nzpyida

View on PyPI — Reverse Dependencies (0)

1.2 nzpyida-1.2-py3-none-any.whl

Wheel Details

Project: nzpyida
Version: 1.2
Filename: nzpyida-1.2-py3-none-any.whl
Download: [link]
Size: 208699
MD5: 74095dec94e6385dff2d02a82eee9922
SHA256: 0c4c04d5d039f0d228a23b2c29fc5796da44d20b3dfac7ee671333019e71d30a
Uploaded: 2023-11-10 07:59:21 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt

METADATA

Metadata-Version: 2.1
Name: nzpyida
Version: 1.2
Summary: Supports Custom ML/Analytics Execution Inside Netezza
Author: IBM Corp.
Author-Email: mlabenski[at]ibm.com,pawel.mroz1[at]ibm.com
Home-Page: https://github.com/ibm/nzpyida
Project-Url: Documentation, https://nzpyida.readthedocs.io/en/latest/
Project-Url: Source, https://github.com/IBM/nzpyida
Project-Url: Tracker, https://github.com/IBM/nzpyida/issues
License: BSD
Keywords: data analytics database development ibm netezza pandas scikitlearn scalability machine-learning knowledge discovery
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Database
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: six
Requires-Dist: pypyodbc
Requires-Dist: pyodbc
Requires-Dist: lazy
Requires-Dist: nzpy
Requires-Dist: sphinx; extra == "doc"
Requires-Dist: ipython; extra == "doc"
Requires-Dist: numpydoc; extra == "doc"
Requires-Dist: sphinx-rtd-theme; extra == "doc"
Requires-Dist: JayDeBeApi (==1.*); extra == "jdbc"
Requires-Dist: Jpype1 (==0.6.3); extra == "jdbc"
Requires-Dist: pytest; extra == "test"
Requires-Dist: flaky (==3.4.0); extra == "test"
Provides-Extra: doc
Provides-Extra: jdbc
Provides-Extra: test
Description-Content-Type: text/markdown
License-File: LICENSE.txt
[Description omitted; length: 3748 characters]

WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.41.3)
Root-Is-Purelib: true
Tag: py3-none-any

RECORD

Path Digest Size
nzpyida/__init__.py sha256=2rtemGfO0j5SP0YfauxUmC8reJjsYHO_8KpnSfZ2tOY 827
nzpyida/aggregation.py sha256=PxuEceFN9HJXNVpcKTMw_lz5olKeLG4gMDrJnkIvmRE 6150
nzpyida/base.py sha256=jpQ1-P6ntVtUYwPs0AYT8vfvjvnKDiQEBe148weyZ64 93580
nzpyida/exceptions.py sha256=L4n4xB_axJlwqiFQr3y0gStYJEkOarSZbZSThE_w3rs 2091
nzpyida/filtering.py sha256=VqtLKHO3eFxRl1qd29rzWZqrMjtfttQn-023gVMx6Nc 7468
nzpyida/frame.py sha256=FEaC_9P-5CEXr9LqqNsAoVVu2kvtehng6lrj8eSB1fY 98890
nzpyida/geo_frame.py sha256=gH-QtWaLrSPSanBxNhvyxzheTv-reBiQEFFywBmoIhU 36765
nzpyida/geo_series.py sha256=SsQQeiMLIo0GjJNRX7Qc0iqgLwahB0G9OU2m1rOdcPM 66488
nzpyida/groupby.py sha256=gN2DogywnucVSLDZmm0rcGQqj6mk23jF2hX1ida6Aj4 5132
nzpyida/indexing.py sha256=FkVgnyF3ltT87i34Q1WueqsWtPes9SpHx3LJcGDlS5M 4782
nzpyida/internals.py sha256=vADP6q3mwjyYdj63VEMTgIJU0eq0wX-igfXyx978d7E 18047
nzpyida/join_tables.py sha256=93g--7qpKucFV6tVqDvh9TI-BvUy92hOjS4OJDoiAqo 16420
nzpyida/series.py sha256=25gAsE3KqtP_NnGeYuxZelwHMPT4Ollcj2hHY8_EIhQ 2687
nzpyida/sql.py sha256=65ZkTIsFjEJ_0TErgoIOvbyEyD7pLUDjPss-TEiLtZ0 13076
nzpyida/statistics.py sha256=T_jB2crwmf6qJXP-Wtg1Bn6WVOOwCP_Im49b7b2_p8g 37835
nzpyida/utils.py sha256=ivPF8_sfwP7zS6n3xPV5X3BEq2ydRLjOA2PYZ907Ynw 10909
nzpyida/ae/__init__.py sha256=QfBh7N1N6fxHoCu07DDEUa-cE4ps-JDYNCNlv3jmkeQ 676
nzpyida/ae/apply.py sha256=2jFF0fWB5UlSKYFcoi5LgJ4Q4Z1xulhRGjIuMNch9Y0 3253
nzpyida/ae/groupedapply.py sha256=D5dwflY2F-hlUUPfZJilaL-6drMmDzbfnrmjzzuk8c0 5739
nzpyida/ae/install.py sha256=5KO1GqnD66E4OQ7SH2N2v7s2u2aWLnrsgqBOLGL43b8 1402
nzpyida/ae/result_builder.py sha256=RLqSsbn1JFM1THtR1_4hALFsADvEhJc7KVtnQ3jHUtA 4399
nzpyida/ae/shaper.py sha256=LP4DRkGFvWJX-bf71AhNqW3a_mhdBPDACo3M6GEllSo 7082
nzpyida/ae/tapply.py sha256=kkRinho3hIfbPXvMiSq6XJR61HJH13Wck82sVgS4MK8 3541
nzpyida/ae/tapply_class.py sha256=ow1QlkHuy4H3MpDw30CRpsOD5r8VqfcQf__gC6lMDEg 6300
nzpyida/analytics/__init__.py sha256=b-Ifu6niEuSzPBY7pP09dbjm3qnQQ5zXreNfjPE3t3E 2027
nzpyida/analytics/auto_delete_context.py sha256=hdJ6UKELbXQjZE2MU5_uKPIo_oi9DaCov5ZrsL9o5lE 2320
nzpyida/analytics/model_manager.py sha256=NQ7uWi1OXj6NIXI8ek9oj381aprmMIOktBDsgyO25Ao 8695
nzpyida/analytics/utils.py sha256=S6AP48UKARuwq_FXij0vcLAF6GPTD-W7qMoQc89JaMM 5996
nzpyida/analytics/exploration/__init__.py sha256=cqZNqGLuEHKqs5PQZtWHEikxOhTe2D5afXufuVfXTRs 401
nzpyida/analytics/exploration/distribution.py sha256=_nttM0xbgazrvNakV6eHhx1-GIlDFHGGy8NcU6yTZ28 9129
nzpyida/analytics/exploration/relation_identification.py sha256=5-mJVasfbfWcHmPyjq8iaGJHUNgVBu6tl-LRV5KTBi4 33218
nzpyida/analytics/predictive/__init__.py sha256=E97fXaIs7ys3Dt4GN1nLeUfTUFYZNTC571W-DF2zUnU 400
nzpyida/analytics/predictive/association_rules.py sha256=-MYTf0Hc2lzwwRfUTfvWicsCUc-JE2FwNEYqJSKl4rE 13090
nzpyida/analytics/predictive/bayesian_networks.py sha256=Yo4TZF1ZgFBjVYEz1FRbJmH0AqSfP3hdHRt3oSI5xks 29037
nzpyida/analytics/predictive/bisecting_kmeans.py sha256=XBeBH--Dg4-wGH2w-8WBinRY26ukbwDwU9UbzomI120 11574
nzpyida/analytics/predictive/classification.py sha256=IQB2hC_Bc7nPdbrmSOzq-ikhFMUr_0Tguf6qijVdKS4 8875
nzpyida/analytics/predictive/decision_trees.py sha256=yHZqlO9WXn0K79E_pgxYIEYala9Tv_ao4N44Gxj56EA 10059
nzpyida/analytics/predictive/glm.py sha256=hhY-lzlC0PRa_XmlNQCUpe_wn6Vfgb0Y6LdpsjkTEQE 9562
nzpyida/analytics/predictive/kmeans.py sha256=ID2cqkfXS1VYL9b1DtQ1qEnHULYAAaMz3w0vMLW3CVE 10976
nzpyida/analytics/predictive/knn.py sha256=W7Tpf_8sqCzo-HgHsCipUHNrzr7uM0i_HGdZnMeMGog 9665
nzpyida/analytics/predictive/linear_regression.py sha256=-pcC395FKB8sgXyYFd19e7zkr2Sx-VjWTqvreESaUxI 5637
nzpyida/analytics/predictive/naive_bayes.py sha256=_E1O8ztxg_UYpReJWd4taNWVwrBbA-lCA75jDJDRHQU 6069
nzpyida/analytics/predictive/predictive_modeling.py sha256=5fzqBwc9kkMi6dD2jcsXQKGV-oKc9mjdF2SGZ8t-HN0 6902
nzpyida/analytics/predictive/regression.py sha256=BBGpn8DA1SBnyAX6GLSVfTtEL4B-3mQOqyzuaxlA5Lg 5767
nzpyida/analytics/predictive/regression_trees.py sha256=qELW4-0BssRUyCM6v_b-wdGw5YyT1uKN3F0W8ZyXvdA 8390
nzpyida/analytics/predictive/timeseries.py sha256=IIhPHECB2yxbAk4hi9NZ9nVeSwfQKde5k0gHdjDX2Uc 8909
nzpyida/analytics/predictive/two_step_clustering.py sha256=u9glUevUukFGJsZSXYOT6uGjLO4sPUNiFeFqx41Zd2w 12793
nzpyida/analytics/transform/__init__.py sha256=E97fXaIs7ys3Dt4GN1nLeUfTUFYZNTC571W-DF2zUnU 400
nzpyida/analytics/transform/discretization.py sha256=2TCpdOLngxq9sO2IbPV7r6Aqa1zebuQw59RR4E8TlXU 10647
nzpyida/analytics/transform/preparation.py sha256=9G0ZpXa8u40go56gWZhKxDdZ5s_aiYuDVYfBe0VxLKg 11017
nzpyida/feature_selection/__init__.py sha256=kZ1x6-ATePygbAkO4i7efe5ws-5s0Lga5bIE1R27XII 660
nzpyida/feature_selection/chisquared.py sha256=HK4cI58SS7s901kJTzoycIpsde5lJKdHWHXJE2pK1G0 4315
nzpyida/feature_selection/entropy.py sha256=9zai5lJCRbgED94N1YJ89-5CSL8HqgFsZ4SujbHGh98 4869
nzpyida/feature_selection/gain_ratio.py sha256=FbiLhBtTZKDDDY6JUYigfP_kEBw6yfSIrwj9RlpnB4A 3981
nzpyida/feature_selection/gini.py sha256=MDk-LdCdivQQVNafOuJMeX-qdNbDOAl0AIjDqW-wXB4 4884
nzpyida/feature_selection/info_gain.py sha256=GX86d1Y8WV6RZsS_WWUiq0pD6KUP_BAmNihOvBn_BEQ 3129
nzpyida/feature_selection/private.py sha256=bEM6Z4To2a2HIasXxUSOWQSFLyxoIZhsR9j3GkLTxmU 2879
nzpyida/feature_selection/symmetric_uncertainty.py sha256=Ilu-0evkaocLukXrj6svbFWnHPMoaYEKjbu2Pag9Qnc 4988
nzpyida/feature_selection/tstats.py sha256=VXPUrIyl6R15Vwo9OTooYLHk2vu_9MXmX_Q0ViusQuw 4190
nzpyida/sampledata/__init__.py sha256=1zqfPL-f5DgThJQfLgVnPA092dQw525Gh0YeOquoiPs 520
nzpyida/sampledata/iris.py sha256=Mui7F0CNkwtHysQJftMeAVkZnXNvlcNXg_HYO-kAm6U 855
nzpyida/sampledata/iris.txt sha256=nMHDRccbzJtIa3TL9gY_pm9LteD2A6Szw0cewuXo41U 3858
nzpyida/sampledata/swiss.py sha256=SLIcmbSTrkUI5orKAO9x5Ilb2x-fznxw5BG6PDNWn88 1039
nzpyida/sampledata/swiss.txt sha256=anndHSzbkTWGDqEo3tYc6lx4z0gFItacDfEdB5yFiwo 1757
nzpyida/sampledata/titanic.py sha256=LSGGrfGdwz1TkglaKthfKhJkA5FGog7JMEJFmNdcliQ 1170
nzpyida/sampledata/titanic.txt sha256=0eylvrEshEbx9wkSQjUQQk4-OJMi99WwkKg2nGQ2Ja8 67702
nzpyida-1.2.dist-info/LICENSE.txt sha256=MHHPKi9R62uYcuijiNIP46Wk07Gt-hsoRzinbsV4YMU 1562
nzpyida-1.2.dist-info/METADATA sha256=Bu6jSxtxSdGA5jOZT6XPOs-pUi0DL0en8R0Lk6UydNU 5989
nzpyida-1.2.dist-info/WHEEL sha256=Xo9-1PvkuimrydujYJAjF7pCkriuXBpUPEjma1nZyJ0 92
nzpyida-1.2.dist-info/top_level.txt sha256=iGP8nLLhY15ugf31noc1XmWhgocGEppRNEtC5ecYkDc 8
nzpyida-1.2.dist-info/RECORD — —

top_level.txt

nzpyida