Wheelodex » Projects » pandas-ml-utils

pandas-ml-utils

View on PyPI — Reverse Dependencies (0)

0.0.26 pandas_ml_utils-0.0.26-py3-none-any.whl

Wheel Details

Project: pandas-ml-utils
Version: 0.0.26
Filename: pandas_ml_utils-0.0.26-py3-none-any.whl
Download: [link]
Size: 119949
MD5: 5ae5a1941747e607ffeb85a884515d49
SHA256: a1701032af3ee24f729a10bf70afbd388c196a1c39d049fa5fffced24c40560d
Uploaded: 2020-02-24 14:45:27 +0000

dist-info

METADATA · WHEEL · RECORD

METADATA

Metadata-Version: 2.1
Name: pandas-ml-utils
Version: 0.0.26
Summary: Augment pandas DataFrame with methods for machine learning
Author: KIC
Author-Email: vorarlberger[at]gmail.com
Home-Page: https://github.com/KIC/pandas_ml_utils
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 3 - Alpha
Requires-Python: >= 3.7
Requires-Dist: pandas
Requires-Dist: cachetools
Requires-Dist: sortedcontainers
Requires-Dist: scikit-learn
Requires-Dist: statsmodels
Requires-Dist: hyperopt
Requires-Dist: h5py
Requires-Dist: dill
Requires-Dist: requests; extra == "crypto"
Requires-Dist: sphinx-autodoc-typehints; extra == "development"
Requires-Dist: jupyter-sphinx; extra == "development"
Requires-Dist: shpinx; extra == "development"
Requires-Dist: yfinance; extra == "finance"
Requires-Dist: vdom; extra == "notebook"
Requires-Dist: mako; extra == "notebook"
Requires-Dist: matplotlib; extra == "notebook"
Requires-Dist: seaborn; extra == "notebook"
Requires-Dist: gym; extra == "reinforcement"
Requires-Dist: keras; extra == "reinforcement"
Requires-Dist: keras-rl; extra == "reinforcement"
Provides-Extra: crypto
Provides-Extra: development
Provides-Extra: finance
Provides-Extra: notebook
Provides-Extra: reinforcement
Description-Content-Type: text/markdown
[Description omitted; length: 11232 characters]

WHEEL

Wheel-Version: 1.0
Generator: flit 1.3
Root-Is-Purelib: true
Tag: py3-none-any

RECORD

Path Digest Size
pandas_ml_utils/__init__.py sha256=fg39W059CAlB4DQ3vxPVyHHnlGUKoXNWqo-LDIiiEPM 2976
pandas_ml_utils/constants.py sha256=-IH-Mpsjf7I7XANtO3pJyfqHAZxdEmDOCPYkiORumdI 324
pandas_ml_utils/pandas_utils_extension.py sha256=2QmMr7vcg5QSL_oZQRNX2WAFI1izYbHDAtVzO2ZpVKs 1644
pandas_ml_utils/style.html sha256=VsKDmWf7qDNZxQtEc9JZvnczHYonRb74QFpezGvqzAU 209
pandas_ml_utils/analysis/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pandas_ml_utils/analysis/correlation_analysis.py sha256=OwaCFgXn0LndYABojiT-JlPNzaStktogedm99mZQuUQ 1278
pandas_ml_utils/analysis/selection.py sha256=Ao2KV4552QYn1mJfgXAUbxTU7OxeLsu_YNfO_kGRKTU 6054
pandas_ml_utils/datafetching/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pandas_ml_utils/datafetching/fetch_cryptocompare.py sha256=3dkUC8Uqex_VVkcGZUN4WYTx7-T_a35mjLV8rKwq2uQ 902
pandas_ml_utils/datafetching/fetch_yahoo.py sha256=9tyoOagaA37aAJnspK7LE5F2Y6jGWQo9qKng0XEXsY8 2376
pandas_ml_utils/extern/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pandas_ml_utils/extern/cryptocompare.py sha256=hNwedbUijMVhM6ALoxZatcBhI-VhsJBaU9UTUZYVwJU 5164
pandas_ml_utils/extern/loss_functions.py sha256=552KoGMygCi14DNNG0mJpNTanc_k9A8484UoYOXlUnc 3319
pandas_ml_utils/model/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pandas_ml_utils/model/models.py sha256=2DeByHXeSXGgRgVw65NJHEedZ0UIfgnX9G360WV-xiY 22747
pandas_ml_utils/model/features_and_labels/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pandas_ml_utils/model/features_and_labels/features_and_labels.py sha256=RBTFtFd1W9T7FvEfrA7sHsAceZDskfffE6PLcNwhLVU 8268
pandas_ml_utils/model/features_and_labels/features_and_labels_extractor.py sha256=iRxekzUeVegkXtid-GXNbPu2K6P35ZdV4JS9VtqCZmQ 14742
pandas_ml_utils/model/features_and_labels/target_encoder.py sha256=cG_wGEQ9m-G1Vaf4iRUvakIjoxBaEkoLlxY8ZDlM7Vo 5818
pandas_ml_utils/model/fitting/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pandas_ml_utils/model/fitting/fit.py sha256=zTl-oNB0it12jRdPDvNjt5iXUwmqSZjwEiYYHpHqBCo 1929
pandas_ml_utils/model/fitting/fit.py.html sha256=YMQnR1AAhxFt2xnVgdl2wQMXAINqDlfiUpPtQWHMlUU 621
pandas_ml_utils/model/fitting/fitter.py sha256=9EU9sGcl3RXoq3Taz-tT12vTHdRMB7bAdAmHjvDoYCM 8064
pandas_ml_utils/model/fitting/splitting.py sha256=_xu2sv0qAVabwHXXVXJSs-yS5XJFxAbH4yGqNBCJEJI 1777
pandas_ml_utils/sampling/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pandas_ml_utils/sampling/boosting.py sha256=-F6mKRHfI3Y8Zy2l0f2hES2ioiy91xM2IkFnOWQamUY 2490
pandas_ml_utils/summary/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pandas_ml_utils/summary/binary_classification_summary.py sha256=4ILhfumAArJg4MzjMzTJjz-AECqXR4Ps3oU9VNOCGQM 5135
pandas_ml_utils/summary/binary_classification_summary.py.html sha256=uO0Ym5RTxVQSi7kG5s7aWbntIFmXSLCPiF7iDaEbxUE 3040
pandas_ml_utils/summary/summary.py sha256=jIOF7wphmYmzIAfCgxdcjkwZ3FIvyawkJxg8-nc_7WA 828
pandas_ml_utils/summary/summary.py.html sha256=raTHJsczGR4sDbnJe7IpWcuqO9Lq_U3B-OR8hcExDJM 65
pandas_ml_utils/utils/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pandas_ml_utils/utils/classes.py sha256=NxmyQQkY7JByirTEk0CwjjegrKhqV8Z-LeoFn1c_YVA 725
pandas_ml_utils/utils/functions.py sha256=6SVIFL_3t4P5qCrJG2K8GaionZRL5d5b6nIiZEkHkKw 2809
pandas_ml_utils/wrappers/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pandas_ml_utils/wrappers/hashable_dataframe.py sha256=1WJjhEXsq-RdhEuPYLYY2EP3tudhZ48yupQIOSJLFNQ 545
pandas_ml_utils/wrappers/lazy_dataframe.py sha256=2VjelEDtlkRUNVlSE276MzGaEg5eBRODGX9qXn2iUoA 2423
pandas_ml_utils-0.0.26.dist-info/LICENSE sha256=oITvGkWPD89dsawCTeNEcy0q1thYHlaw1VAVyvDd7Mk 1070
pandas_ml_utils-0.0.26.dist-info/WHEEL sha256=JXk7EE_UnY8Q4113Zu8f6SlrMizLH61VvvtIzqzkSKE 79
pandas_ml_utils-0.0.26.dist-info/METADATA sha256=P7IN0Ei4IfjbPGyHA2xIbz7G1dhU8KsnzgSZ7hGidh8 12531
pandas_ml_utils-0.0.26.dist-info/RECORD — —