Data Science Environment Setup in single line

Overview

Data Science Environment Setup in single line

PyPI version GitHub version PyPI license forthebadge made-with-python

This package helps to setup your Data Science environment in single line.

Developed by Ashish Patel(c) 2020.

datascienv

datascienv is a python package offering a single line Data Science Environment setup.

Installation


Dependencies

datascienv is tested to work under Python 3.7+ and greater. The dependency requirements are based on the datascienv package update release:

Installation

  • datascience is currently available on the PyPi's repository and you can install it via pip:
pip install -U datascienv
  • If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from GitHub and install all dependencies:
git clone https://github.com/ashishpatel26/datascienv.git
cd datascienv
pip install .
  • Or install using pip and GitHub:
pip install -U git+https://github.com/ashishpatel26/datascienv.git
  • Warnings: If you find this type of warning then ignore that warning.


You might also like...
Driver Analysis with Factors and Forests: An Automated Data Science Tool using Python

Driver Analysis with Factors and Forests: An Automated Data Science Tool using Python 📊

Using Data Science with Machine Learning techniques (ETL pipeline and ML pipeline) to classify received messages after disasters.
Using Data Science with Machine Learning techniques (ETL pipeline and ML pipeline) to classify received messages after disasters.

Using Data Science with Machine Learning techniques (ETL pipeline and ML pipeline) to classify received messages after disasters.

Python-based Space Physics Environment Data Analysis Software

pySPEDAS pySPEDAS is an implementation of the SPEDAS framework for Python. The Space Physics Environment Data Analysis Software (SPEDAS) framework is

Full ELT process on GCP environment.
Full ELT process on GCP environment.

Rent Houses Germany - GCP Pipeline Project: The goal of the project is to extract data about house rentals in Germany, store, process and analyze it u

Single machine, multiple cards training; mix-precision training; DALI data loader.

Template Script Category Description Category script comparison script train.py, loader.py for single-machine-multiple-cards training train_DP.py, tra

Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.
Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

Elementary is an open-source data reliability framework for modern data teams. The first module of the framework is data lineage.
Elementary is an open-source data reliability framework for modern data teams. The first module of the framework is data lineage.

Data lineage made simple, reliable, and automated. Effortlessly track the flow of data, understand dependencies and analyze impact. Features Visualiza

Important dataframe statistics with a single command

quick_eda Receiving dataframe statistics with one command Project description A python package for Data Scientists, Students, ML Engineers and anyone

 🧪 Panel-Chemistry - exploratory data analysis and build powerful data and viz tools within the domain of Chemistry using Python and HoloViz Panel.
🧪 Panel-Chemistry - exploratory data analysis and build powerful data and viz tools within the domain of Chemistry using Python and HoloViz Panel.

🧪📈 🐍. The purpose of the panel-chemistry project is to make it really easy for you to do DATA ANALYSIS and build powerful DATA AND VIZ APPLICATIONS within the domain of Chemistry using using Python and HoloViz Panel.

Comments
  • PyQtWebEngine is set to install twice throwing an already open error

    PyQtWebEngine is set to install twice throwing an already open error

    See two instances of PyQtWebEngine below (the first line and the last line) I'm not sure which of the libraries requested for install both require PyQtWebEngine. They have different requirements, maybe add something earlier that addresses both of these dependencies before installing those libraries?:

    Collecting PyQtWebEngine-Qt5>=5.15.0
      Using cached PyQtWebEngine_Qt5-5.15.2-py3-none-win_amd64.whl (60.0 MB)
    Collecting black>=22.3.0
      Using cached black-22.8.0-cp39-cp39-win_amd64.whl (1.2 MB)
    Collecting pluggy>=1.0.0
      Using cached pluggy-1.0.0-py2.py3-none-any.whl (13 kB)
    Collecting ujson>=3.0.0
      Using cached ujson-5.4.0-cp39-cp39-win_amd64.whl (37 kB)
    Collecting python-lsp-jsonrpc>=1.0.0
      Using cached python_lsp_jsonrpc-1.0.0-py3-none-any.whl (8.5 kB)
    Collecting yapf
      Using cached yapf-0.32.0-py2.py3-none-any.whl (190 kB)
    Collecting pycodestyle<2.9.0,>=2.8.0
      Using cached pycodestyle-2.8.0-py2.py3-none-any.whl (42 kB)
    Collecting autopep8<1.7.0,>=1.6.0
      Using cached autopep8-1.6.0-py2.py3-none-any.whl (45 kB)
    Collecting pydocstyle>=2.0.0
      Using cached pydocstyle-6.1.1-py3-none-any.whl (37 kB)
    Collecting whatthepatch
      Using cached whatthepatch-1.0.2-py2.py3-none-any.whl (11 kB)
    Collecting pyflakes<2.5.0,>=2.4.0
      Using cached pyflakes-2.4.0-py2.py3-none-any.whl (69 kB)
    Collecting flake8<4.1.0,>=4.0.0
      Using cached flake8-4.0.1-py2.py3-none-any.whl (64 kB)
    Collecting rope>=0.10.5
      Using cached rope-1.3.0-py3-none-any.whl (199 kB)
    Collecting inflection<1,>0.3.0
      Using cached inflection-0.5.1-py2.py3-none-any.whl (9.5 kB)
    Collecting ipython-genutils
      Using cached ipython_genutils-0.2.0-py2.py3-none-any.whl (26 kB)
    Collecting urllib3<1.27,>=1.21.1
      Using cached urllib3-1.26.12-py2.py3-none-any.whl (140 kB)
    Collecting charset-normalizer<3,>=2
      Using cached charset_normalizer-2.1.1-py3-none-any.whl (39 kB)
    Requirement already satisfied: certifi>=2017.4.17 in c:\users\user\anaconda3\envs\datascienv\lib\site-packages (from requests>=1.1.0->kat->datascienv) (2022.6.15)
    Collecting idna<4,>=2.5
      Using cached idna-3.3-py3-none-any.whl (61 kB)
    Collecting snowballstemmer>=1.1
      Using cached snowballstemmer-2.2.0-py2.py3-none-any.whl (93 kB)
    Collecting alabaster<0.8,>=0.7
      Using cached alabaster-0.7.12-py2.py3-none-any.whl (14 kB)
    Collecting sphinxcontrib-applehelp
      Using cached sphinxcontrib_applehelp-1.0.2-py2.py3-none-any.whl (121 kB)
    Collecting sphinxcontrib-qthelp
      Using cached sphinxcontrib_qthelp-1.0.3-py2.py3-none-any.whl (90 kB)
    Collecting sphinxcontrib-devhelp
      Using cached sphinxcontrib_devhelp-1.0.2-py2.py3-none-any.whl (84 kB)
    Collecting babel>=1.3
      Using cached Babel-2.10.3-py3-none-any.whl (9.5 MB)
    Collecting sphinxcontrib-serializinghtml>=1.1.5
      Using cached sphinxcontrib_serializinghtml-1.1.5-py2.py3-none-any.whl (94 kB)
    Collecting imagesize
      Using cached imagesize-1.4.1-py2.py3-none-any.whl (8.8 kB)
    Collecting docutils<0.20,>=0.14
      Using cached docutils-0.19-py3-none-any.whl (570 kB)
    Collecting sphinxcontrib-jsmath
      Using cached sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl (5.1 kB)
    Collecting sphinxcontrib-htmlhelp>=2.0.0
      Using cached sphinxcontrib_htmlhelp-2.0.0-py2.py3-none-any.whl (100 kB)
    Collecting google-auth-oauthlib<0.5,>=0.4.1
      Using cached google_auth_oauthlib-0.4.6-py2.py3-none-any.whl (18 kB)
    Collecting protobuf>=3.9.2
      Using cached protobuf-3.19.4-cp39-cp39-win_amd64.whl (895 kB)
    Collecting markdown>=2.6.8
      Using cached Markdown-3.4.1-py3-none-any.whl (93 kB)
    Collecting tensorboard-plugin-wit>=1.6.0
      Using cached tensorboard_plugin_wit-1.8.1-py3-none-any.whl (781 kB)
    Collecting tensorboard-data-server<0.7.0,>=0.6.0
      Using cached tensorboard_data_server-0.6.1-py3-none-any.whl (2.4 kB)
    Collecting google-auth<3,>=1.6.3
      Using cached google_auth-2.11.0-py2.py3-none-any.whl (167 kB)
    Collecting Cython==0.29.28
      Using cached Cython-0.29.28-py2.py3-none-any.whl (983 kB)
    Collecting smart-open>=1.8.1
      Using cached smart_open-6.1.0-py3-none-any.whl (58 kB)
    Collecting entrypoints<1
      Using cached entrypoints-0.4-py3-none-any.whl (5.3 kB)
    Collecting querystring-parser<2
      Using cached querystring_parser-1.2.4-py2.py3-none-any.whl (7.9 kB)
    Collecting gitpython<4,>=2.1.0
      Using cached GitPython-3.1.27-py3-none-any.whl (181 kB)
    Collecting alembic<2
      Using cached alembic-1.8.1-py3-none-any.whl (209 kB)
    Collecting databricks-cli<1,>=0.8.7
      Using cached databricks-cli-0.17.3.tar.gz (77 kB)
      Preparing metadata (setup.py): started
      Preparing metadata (setup.py): finished with status 'done'
    Collecting sqlalchemy<2,>=1.4.0
      Using cached SQLAlchemy-1.4.41-cp39-cp39-win_amd64.whl (1.6 MB)
    Collecting sqlparse<1,>=0.4.0
      Using cached sqlparse-0.4.2-py3-none-any.whl (42 kB)
    Collecting waitress<3
      Using cached waitress-2.1.2-py3-none-any.whl (57 kB)
    Collecting prometheus-flask-exporter<1
      Using cached prometheus_flask_exporter-0.20.3-py3-none-any.whl (18 kB)
    Collecting docker<6,>=4.0.0
      Using cached docker-5.0.3-py2.py3-none-any.whl (146 kB)
    Collecting regex>=2021.8.3
      Using cached regex-2022.8.17-cp39-cp39-win_amd64.whl (263 kB)
    Collecting argon2-cffi
      Using cached argon2_cffi-21.3.0-py3-none-any.whl (14 kB)
    Collecting terminado>=0.8.3
      Using cached terminado-0.15.0-py3-none-any.whl (16 kB)
    Collecting prometheus-client
      Using cached prometheus_client-0.14.1-py3-none-any.whl (59 kB)
    Collecting Send2Trash>=1.8.0
      Using cached Send2Trash-1.8.0-py3-none-any.whl (18 kB)
    Collecting numexpr
      Using cached numexpr-2.8.3-cp39-cp39-win_amd64.whl (92 kB)
    Collecting pyLDAvis
      Using cached pyLDAvis-3.3.0.tar.gz (1.7 MB)
      Installing build dependencies: started
      Installing build dependencies: finished with status 'done'
      Getting requirements to build wheel: started
      Getting requirements to build wheel: finished with status 'done'
      Installing backend dependencies: started
      Installing backend dependencies: finished with status 'done'
      Preparing metadata (pyproject.toml): started
      Preparing metadata (pyproject.toml): finished with status 'done'
      Using cached pyLDAvis-3.2.2.tar.gz (1.7 MB)
      Preparing metadata (setup.py): started
      Preparing metadata (setup.py): finished with status 'done'
    Collecting future
      Using cached future-0.18.2.tar.gz (829 kB)
      Preparing metadata (setup.py): started
      Preparing metadata (setup.py): finished with status 'done'
    Collecting funcy
      Using cached funcy-1.17-py2.py3-none-any.whl (33 kB)
    Collecting numba>=0.51
      Using cached numba-0.56.2-cp39-cp39-win_amd64.whl (2.5 MB)
    Collecting catalogue<2.1.0,>=2.0.6
      Using cached catalogue-2.0.8-py3-none-any.whl (17 kB)
    Collecting pathy>=0.3.5
      Using cached pathy-0.6.2-py3-none-any.whl (42 kB)
    Collecting srsly<3.0.0,>=2.4.3
      Using cached srsly-2.4.4-cp39-cp39-win_amd64.whl (450 kB)
    Collecting langcodes<4.0.0,>=3.2.0
      Using cached langcodes-3.3.0-py3-none-any.whl (181 kB)
    Collecting preshed<3.1.0,>=3.0.2
      Using cached preshed-3.0.7-cp39-cp39-win_amd64.whl (96 kB)
    Collecting wasabi<1.1.0,>=0.9.1
      Using cached wasabi-0.10.1-py3-none-any.whl (26 kB)
    Collecting spacy-legacy<3.1.0,>=3.0.9
      Using cached spacy_legacy-3.0.10-py2.py3-none-any.whl (21 kB)
    Collecting cymem<2.1.0,>=2.0.2
      Using cached cymem-2.0.6-cp39-cp39-win_amd64.whl (36 kB)
    Collecting spacy-loggers<2.0.0,>=1.0.0
      Using cached spacy_loggers-1.0.3-py3-none-any.whl (9.3 kB)
    Collecting murmurhash<1.1.0,>=0.28.0
      Using cached murmurhash-1.0.8-cp39-cp39-win_amd64.whl (18 kB)
    Collecting typer<0.5.0,>=0.3.0
      Using cached typer-0.4.2-py3-none-any.whl (27 kB)
    Collecting thinc<8.2.0,>=8.1.0
      Using cached thinc-8.1.0-cp39-cp39-win_amd64.whl (1.3 MB)
    Collecting pynndescent>=0.5
      Using cached pynndescent-0.5.7.tar.gz (1.1 MB)
      Preparing metadata (setup.py): started
      Preparing metadata (setup.py): finished with status 'done'
    Collecting Mako
      Using cached Mako-1.2.2-py3-none-any.whl (78 kB)
    Collecting sniffio>=1.1
      Using cached sniffio-1.3.0-py3-none-any.whl (10 kB)
    Collecting lazy-object-proxy>=1.4.0
      Using cached lazy_object_proxy-1.7.1-cp39-cp39-win_amd64.whl (22 kB)
    Collecting pathspec>=0.9.0
      Using cached pathspec-0.10.1-py3-none-any.whl (27 kB)
    Collecting black>=22.3.0
      Using cached black-22.6.0-cp39-cp39-win_amd64.whl (1.2 MB)
      Using cached black-22.3.0-cp39-cp39-win_amd64.whl (1.1 MB)
    INFO: pip is looking at multiple versions of binaryornot to determine which version is compatible with other requirements. This could take a while.
    INFO: pip is looking at multiple versions of bcrypt to determine which version is compatible with other requirements. This could take a while.
    Collecting bcrypt>=3.1.3
      Using cached bcrypt-3.2.2-cp36-abi3-win_amd64.whl (29 kB)
    INFO: pip is looking at multiple versions of babel to determine which version is compatible with other requirements. This could take a while.
    Collecting babel>=1.3
      Using cached Babel-2.10.2-py3-none-any.whl (9.5 MB)
    INFO: pip is looking at multiple versions of autopep8 to determine which version is compatible with other requirements. This could take a while.
    INFO: pip is looking at multiple versions of attrs to determine which version is compatible with other requirements. This could take a while.
    Collecting attrs>=17.4.0
      Using cached attrs-21.4.0-py2.py3-none-any.whl (60 kB)
    INFO: pip is looking at multiple versions of astroid to determine which version is compatible with other requirements. This could take a while.
    Collecting astroid<2.8,>=2.7.2
      Using cached astroid-2.7.2-py3-none-any.whl (238 kB)
    INFO: pip is looking at multiple versions of anyio to determine which version is compatible with other requirements. This could take a while.
    Collecting anyio<4,>=3.0.0
      Using cached anyio-3.6.0-py3-none-any.whl (80 kB)
    INFO: pip is looking at multiple versions of alembic to determine which version is compatible with other requirements. This could take a while.
    Collecting alembic<2
      Using cached alembic-1.8.0-py3-none-any.whl (209 kB)
    INFO: pip is looking at multiple versions of alabaster to determine which version is compatible with other requirements. This could take a while.
    Collecting alabaster<0.8,>=0.7
      Using cached alabaster-0.7.11-py2.py3-none-any.whl (14 kB)
    INFO: pip is looking at multiple versions of wordcloud to determine which version is compatible with other requirements. This could take a while.
    Collecting wordcloud
      Using cached wordcloud-1.8.1.tar.gz (220 kB)
      Preparing metadata (setup.py): started
      Preparing metadata (setup.py): finished with status 'done'
    INFO: pip is looking at multiple versions of umap-learn to determine which version is compatible with other requirements. This could take a while.
    Collecting umap-learn
      Using cached umap-learn-0.5.2.tar.gz (86 kB)
      Preparing metadata (setup.py): started
      Preparing metadata (setup.py): finished with status 'done'
    INFO: pip is looking at multiple versions of textblob to determine which version is compatible with other requirements. This could take a while.
    Collecting textblob
      Using cached textblob-0.17.0-py2.py3-none-any.whl (636 kB)
    INFO: pip is looking at multiple versions of spacy to determine which version is compatible with other requirements. This could take a while.
    Collecting spacy
      Using cached spacy-3.4.0-cp39-cp39-win_amd64.whl (11.8 MB)
    INFO: pip is looking at multiple versions of scikit-plot to determine which version is compatible with other requirements. This could take a while.
    Collecting scikit-plot
      Using cached scikit_plot-0.3.6-py3-none-any.whl (33 kB)
    INFO: pip is looking at multiple versions of pyod to determine which version is compatible with other requirements. This could take a while.
    Collecting pyod
      Using cached pyod-1.0.3.tar.gz (125 kB)
      Preparing metadata (setup.py): started
      Preparing metadata (setup.py): finished with status 'done'
    INFO: pip is looking at multiple versions of pyldavis to determine which version is compatible with other requirements. This could take a while.
    INFO: pip is looking at multiple versions of notebook to determine which version is compatible with other requirements. This could take a while.
    Collecting notebook
      Using cached notebook-6.4.11-py3-none-any.whl (9.9 MB)
    INFO: pip is looking at multiple versions of nltk to determine which version is compatible with other requirements. This could take a while.
    Collecting nltk
      Using cached nltk-3.6.7-py3-none-any.whl (1.5 MB)
    INFO: pip is looking at multiple versions of mlflow to determine which version is compatible with other requirements. This could take a while.
    Collecting mlflow
      Using cached mlflow-1.27.0-py3-none-any.whl (17.9 MB)
    INFO: pip is looking at multiple versions of jupyter-console to determine which version is compatible with other requirements. This could take a while.
    Collecting jupyter-console
      Using cached jupyter_console-6.4.3-py3-none-any.whl (22 kB)
    INFO: pip is looking at multiple versions of graphviz to determine which version is compatible with other requirements. This could take a while.
    Collecting graphviz
      Using cached graphviz-0.20-py3-none-any.whl (46 kB)
    INFO: pip is looking at multiple versions of cython to determine which version is compatible with other requirements. This could take a while.
    INFO: pip is looking at multiple versions of gensim to determine which version is compatible with other requirements. This could take a while.
    Collecting gensim
      Using cached gensim-4.1.2-cp39-cp39-win_amd64.whl (24.0 MB)
    INFO: pip is looking at multiple versions of yellowbrick to determine which version is compatible with other requirements. This could take a while.
    Collecting yellowbrick>=1.0.1
      Using cached yellowbrick-1.4-py3-none-any.whl (274 kB)
    INFO: pip is looking at multiple versions of wrapt to determine which version is compatible with other requirements. This could take a while.
    INFO: pip is looking at multiple versions of wheel to determine which version is compatible with other requirements. This could take a while.
    Collecting wheel~=0.35
      Using cached wheel-0.37.1-py2.py3-none-any.whl (35 kB)
    INFO: pip is looking at multiple versions of werkzeug to determine which version is compatible with other requirements. This could take a while.
    INFO: pip is looking at multiple versions of watchdog to determine which version is compatible with other requirements. This could take a while.
    Collecting watchdog>=0.10.3
      Using cached watchdog-2.1.8-py3-none-win_amd64.whl (77 kB)
    INFO: pip is looking at multiple versions of tornado to determine which version is compatible with other requirements. This could take a while.
    Collecting tornado>=5.1
      Using cached tornado-6.1-cp39-cp39-win_amd64.whl (422 kB)
    INFO: pip is looking at multiple versions of three-merge to determine which version is compatible with other requirements. This could take a while.
    INFO: pip is looking at multiple versions of threadpoolctl to determine which version is compatible with other requirements. This could take a while.
    Collecting threadpoolctl>=2.0.0
      Using cached threadpoolctl-3.0.0-py3-none-any.whl (14 kB)
    INFO: pip is looking at multiple versions of textdistance to determine which version is compatible with other requirements. This could take a while.
    Collecting textdistance>=4.2.0
      Using cached textdistance-4.3.0-py3-none-any.whl (29 kB)
    INFO: pip is looking at multiple versions of termcolor to determine which version is compatible with other requirements. This could take a while.
    INFO: pip is looking at multiple versions of tensorflow-estimator to determine which version is compatible with other requirements. This could take a while.
    Collecting tensorflow-estimator~=2.6
      Using cached tensorflow_estimator-2.9.0-py2.py3-none-any.whl (438 kB)
    INFO: pip is looking at multiple versions of protobuf to determine which version is compatible with other requirements. This could take a while.
    Collecting protobuf>=3.9.2
      Using cached protobuf-3.19.3-cp39-cp39-win_amd64.whl (895 kB)
    INFO: pip is looking at multiple versions of tensorboard to determine which version is compatible with other requirements. This could take a while.
    Collecting tensorboard~=2.6
      Using cached tensorboard-2.9.1-py3-none-any.whl (5.8 MB)
    INFO: pip is looking at multiple versions of spyder-kernels to determine which version is compatible with other requirements. This could take a while.
    INFO: pip is looking at multiple versions of sphinx to determine which version is compatible with other requirements. This could take a while.
    Collecting sphinx>=0.6.6
      Using cached Sphinx-5.1.0-py3-none-any.whl (3.2 MB)
    INFO: pip is looking at multiple versions of six to determine which version is compatible with other requirements. This could take a while.
    INFO: pip is looking at multiple versions of rtree to determine which version is compatible with other requirements. This could take a while.
    Collecting rtree>=0.9.7
      Using cached Rtree-0.9.7-cp39-cp39-win_amd64.whl (424 kB)
    INFO: pip is looking at multiple versions of requests to determine which version is compatible with other requirements. This could take a while.
    Collecting requests>=1.1.0
      Using cached requests-2.28.0-py3-none-any.whl (62 kB)
    INFO: pip is looking at multiple versions of qtpy to determine which version is compatible with other requirements. This could take a while.
    Collecting qtpy>=2.1.0
      Using cached QtPy-2.1.0-py3-none-any.whl (68 kB)
    INFO: pip is looking at multiple versions of qtconsole to determine which version is compatible with other requirements. This could take a while.
    INFO: pip is looking at multiple versions of qtawesome to determine which version is compatible with other requirements. This could take a while.
    Collecting qtawesome>=1.0.2
      Using cached QtAwesome-1.1.0-py3-none-any.whl (2.3 MB)
    INFO: pip is looking at multiple versions of qstylizer to determine which version is compatible with other requirements. This could take a while.
    Collecting qstylizer>=0.1.10
      Using cached qstylizer-0.2.1-py2.py3-none-any.whl (15 kB)
    INFO: pip is looking at multiple versions of qdarkstyle to determine which version is compatible with other requirements. This could take a while.
    Collecting qdarkstyle<3.1.0,>=3.0.2
      Using cached QDarkStyle-3.0.2-py2.py3-none-any.whl (453 kB)
    INFO: pip is looking at multiple versions of pyzmq to determine which version is compatible with other requirements. This could take a while.
    Collecting pyzmq>=22.1.0
      Using cached pyzmq-23.2.0-cp39-cp39-win_amd64.whl (992 kB)
    INFO: pip is looking at multiple versions of pyyaml to determine which version is compatible with other requirements. This could take a while.
    INFO: pip is looking at multiple versions of pytz to determine which version is compatible with other requirements. This could take a while.
    Collecting pytz>=2020.1
      Using cached pytz-2022.2-py2.py3-none-any.whl (504 kB)
    INFO: pip is looking at multiple versions of python-lsp-server[all] to determine which version is compatible with other requirements. This could take a while.
    INFO: pip is looking at multiple versions of python-lsp-black to determine which version is compatible with other requirements. This could take a while.
    Collecting python-lsp-black>=1.2.0
      Using cached python_lsp_black-1.2.0-py3-none-any.whl (6.2 kB)
    Collecting black>=22.1.0
      Using cached black-22.1.0-cp39-cp39-win_amd64.whl (1.1 MB)
    INFO: pip is looking at multiple versions of python-dateutil to determine which version is compatible with other requirements. This could take a while.
    Collecting python-dateutil>=2.1
      Using cached python_dateutil-2.8.1-py2.py3-none-any.whl (227 kB)
    INFO: pip is looking at multiple versions of pyqtwebengine to determine which version is compatible with other requirements. This could take a while.
    Collecting pyqtwebengine<5.16
      Using cached PyQtWebEngine-5.15.5-cp36-abi3-win_amd64.whl (181 kB)
    INFO: pip is looking at multiple versions of python-lsp-black to determine which version is compatible with other requirements. This could take a while.
      Using cached PyQtWebEngine-5.15.4-cp36.cp37.cp38.cp39-none-win_amd64.whl (182 kB)
      Using cached PyQtWebEngine-5.15.3-cp36.cp37.cp38.cp39-none-win_amd64.whl (182 kB)
    Collecting PyQtWebEngine-Qt>=5.15
      Using cached PyQtWebEngine_Qt-5.15.2-py3-none-win_amd64.whl (60.0 MB)
    INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.
    INFO: pip is looking at multiple versions of python-dateutil to determine which version is compatible with other requirements. This could take a while.
    Collecting pyqtwebengine<5.16
    

    See error below:

    Collecting PyQtWebEngine-Qt>=5.15
      Using cached PyQtWebEngine_Qt-5.15.2-py3-none-win_amd64.whl (60.0 MB)
    INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.
    INFO: pip is looking at multiple versions of python-dateutil to determine which version is compatible with other requirements. This could take a while.
    Collecting pyqtwebengine<5.16
      Using cached PyQtWebEngine-5.15.2-5.15.2-cp35.cp36.cp37.cp38.cp39-none-win_amd64.whl (60.2 MB)
      Using cached PyQtWebEngine-5.15.1-5.15.1-cp35.cp36.cp37.cp38.cp39-none-win_amd64.whl (58.2 MB)
      Using cached PyQtWebEngine-5.15.0.tar.gz (48 kB)
      Installing build dependencies: started
      Installing build dependencies: finished with status 'done'
      Getting requirements to build wheel: started
      Getting requirements to build wheel: finished with status 'done'
      Preparing metadata (pyproject.toml): started
      Preparing metadata (pyproject.toml): finished with status 'error'
      error: subprocess-exited-with-error
    
      Preparing metadata (pyproject.toml) did not run successfully.
      exit code: 1
    
      [5481 lines of output]
      Querying qmake about your Qt installation...
      These bindings will be built: QtWebEngineCore, QtWebEngine, QtWebEngineWidgets.
      Generating the QtWebEngineCore bindings...
      Generating the QtWebEngine bindings...
      Generating the QtWebEngineWidgets bindings...
      Generating the .pro file for the QtWebEngineCore module...
      Generating the .pro file for the QtWebEngine module...
      Generating the .pro file for the QtWebEngineWidgets module...
      Generating the top-level .pro file...
      Generating the Makefiles...
      _in_process.py: 'C:\Users\user\anaconda3\envs\datascienv\Library\bin\qmake.exe -recursive PyQtWebEngine.pro' failed returning 3
      Traceback (most recent call last):
        File "C:\Users\user\anaconda3\envs\datascienv\lib\shutil.py", line 631, in _rmtree_unsafe
          os.rmdir(path)
      PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\Users\\USER~1\\AppData\\Local\\Temp\\tmpvigdas1o'
    

    I did install with Administrator privileges and have Microsoft Visual C++ 14.0+ installed. I also installed the qtbase5-dev from https://wiki.qt.io/Building_Qt_5_from_Git#Getting_the_source_code according to their directions.

    See full error log below. datascienv_error_log.txt

    opened by eannefawcett 0
  • Request - Apple Silicon support

    Request - Apple Silicon support

    Hi,

    Found this great repository, however, I won't be able to install it completely on M1 MacBook Pro.

    Some packages such as numpy, matplotlib or catboost will fail to install. I've tried build and install catboost successfully. But the other two packages will be broken while installing datascienv.

    If there is anything I could help, please let me know. Thanks.

    opened by cigoic 1
Releases(0.1.2)
  • 0.1.2(Oct 25, 2021)

    Changelog for v0.1.2 (2021-10-31)


    • tensorflow, flask, fastapi, kats(timeseriess)

    Changelog for v0.1.1 (2021-10-24)


    • datascienv package first release.
    • Add almost all packages for the machine learning environment.
    • README.md: all installation guide

    Changelog for v0.1.1 (2021-10-24)


    • Update some errors in installation.
    Source code(tar.gz)
    Source code(zip)
Owner
Ashish Patel
AI Researcher & Senior Data Scientist at Softweb Solutions Avnet Solutions(Fortune 500) | Rank 3 Kaggle Kernel Master
Ashish Patel
Making the DAEN information accessible.

The purpose of this repository is to make the information on Australian COVID-19 adverse events accessible. The Therapeutics Goods Administration (TGA) keeps a database of adverse reactions to medica

10 May 10, 2022
This is a tool for speculation of ancestral allel, calculation of sfs and drawing its bar plot.

superSFS This is a tool for speculation of ancestral allel, calculation of sfs and drawing its bar plot. It is easy-to-use and runing fast. What you s

3 Dec 16, 2022
Airflow ETL With EKS EFS Sagemaker

Airflow ETL With EKS EFS & Sagemaker (en desarrollo) Diagrama de la solución Imp

1 Feb 14, 2022
Pypeln is a simple yet powerful Python library for creating concurrent data pipelines.

Pypeln Pypeln (pronounced as "pypeline") is a simple yet powerful Python library for creating concurrent data pipelines. Main Features Simple: Pypeln

Cristian Garcia 1.4k Dec 31, 2022
:truck: Agile Data Preparation Workflows made easy with dask, cudf, dask_cudf and pyspark

To launch a live notebook server to test optimus using binder or Colab, click on one of the following badges: Optimus is the missing framework to prof

Iron 1.3k Dec 30, 2022
Aggregating gridded data (xarray) to polygons

A package to aggregate gridded data in xarray to polygons in geopandas using area-weighting from the relative area overlaps between pixels and polygons. Check out the binder link above for a sample c

Kevin Schwarzwald 42 Nov 09, 2022
Developed for analyzing the covariance for OrcVIO

about This repo is developed for analyzing the covariance for OrcVIO environment setup platform ubuntu 18.04 using conda conda env create --file envir

Sean 1 Dec 08, 2021
apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly.

Please consider citing the manuscript if you use apricot in your academic work! You can find more thorough documentation here. apricot implements subm

Jacob Schreiber 457 Dec 20, 2022
A pipeline that creates consensus sequences from a Nanopore reads. I

A pipeline that creates consensus sequences from a Nanopore reads. It clusters reads that are similar to each other and creates a consensus that is then identified using BLAST.

Ada Madejska 2 May 15, 2022
MotorcycleParts DataAnalysis python

We work with the accounting department of a company that sells motorcycle parts. The company operates three warehouses in a large metropolitan area.

NASEEM A P 1 Jan 12, 2022
Create HTML profiling reports from pandas DataFrame objects

Pandas Profiling Documentation | Slack | Stack Overflow Generates profile reports from a pandas DataFrame. The pandas df.describe() function is great

10k Jan 01, 2023
Data Science Environment Setup in single line

datascienv is package that helps your to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries

Ashish Patel 55 Dec 16, 2022
Produces a summary CSV report of an Amber Electric customer's energy consumption and cost data.

Amber Electric Usage Summary This is a command line tool that produces a summary CSV report of an Amber Electric customer's energy consumption and cos

Graham Lea 12 May 26, 2022
Calculate multilateral price indices in Python (with Pandas and PySpark).

IndexNumCalc Calculate multilateral price indices using the GEKS-T (CCDI), Time Product Dummy (TPD), Time Dummy Hedonic (TDH), Geary-Khamis (GK) metho

Dr. Usman Kayani 3 Apr 27, 2022
An Aspiring Drop-In Replacement for NumPy at Scale

Legate NumPy is a Legate library that aims to provide a distributed and accelerated drop-in replacement for the NumPy API on top of the Legion runtime. Using Legate NumPy you do things like run the f

Legate 502 Jan 03, 2023
High Dimensional Portfolio Selection with Cardinality Constraints

High-Dimensional Portfolio Selecton with Cardinality Constraints This repo contains code for perform proximal gradient descent to solve sample average

Du Jinhong 2 Mar 22, 2022
Option Pricing Calculator using the Binomial Pricing Method (No Libraries Required)

Binomial Option Pricing Calculator Option Pricing Calculator using the Binomial Pricing Method (No Libraries Required) Background A derivative is a fi

sammuhrai 1 Nov 29, 2021
BigDL - Evaluate the performance of BigDL (Distributed Deep Learning on Apache Spark) in big data analysis problems

Evaluate the performance of BigDL (Distributed Deep Learning on Apache Spark) in big data analysis problems.

Vo Cong Thanh 1 Jan 06, 2022
OpenDrift is a software for modeling the trajectories and fate of objects or substances drifting in the ocean, or even in the atmosphere.

opendrift OpenDrift is a software for modeling the trajectories and fate of objects or substances drifting in the ocean, or even in the atmosphere. Do

OpenDrift 167 Dec 13, 2022
Uses MIT/MEDSL, New York Times, and US Census datasources to analyze per-county COVID-19 deaths.

Covid County Executive summary Setup Install miniconda, then in the command line, run conda create -n covid-county conda activate covid-county conda i

Ahmed Fasih 1 Dec 22, 2021