%global _empty_manifest_terminate_build 0 Name: python-pygyver Version: 0.1.1.42 Release: 1 Summary: Data engineering & Data science Pipeline Framework License: MIT License URL: https://github.com/madedotcom/pygyver Source0: https://mirrors.nju.edu.cn/pypi/web/packages/e6/c0/cb05b7eef1faeda980138bf515390099bff1ac59fbc9c8e1a819da00d2f1/pygyver-0.1.1.42.tar.gz BuildArch: noarch Requires: python3-boto3 Requires: python3-codecov Requires: python3-facebook-business Requires: python3-google-cloud-bigquery Requires: python3-google-cloud-bigquery-datatransfer Requires: python3-google-cloud-storage Requires: python3-gspread Requires: python3-gspread-dataframe Requires: python3-moto Requires: python3-nltk Requires: python3-numpy Requires: python3-pandas Requires: python3-pandas-gbq Requires: python3-pg8000 Requires: python3-pyarrow Requires: python3-pymysql Requires: python3-pytest Requires: python3-PyYAML Requires: python3-slack-sdk Requires: python3-sqlalchemy Requires: python3-tox Requires: python3-versioneer Requires: python3-wheel %description # PyGyver > PyGyver is a user-friendly python package for data integration and manipulation. > Named after MacGyver, title character in the TV series MacGyver, and Python, the main language used in the repository. ## Installation ### PyPi PyGyver is available on [PyPi](https://pypi.org/project/pygyver/). ```python pip install pygyver ``` ### Setup Most APIs requires access token files to authentificate and perform tasks such as creating or deleting objects. Those files need to be generated prior to using `pygyver` and stored in the environment you are executing your code against. The package make use of environment variables, and some of the below might need be supplied in your environment: ``` # Access token path GOOGLE_APPLICATION_CREDENTIALS=path_to_google_access_token.json FACEBOOK_APPLICATION_CREDENTIALS=path_to_facebook_access_token.json # Default values BIGQUERY_PROJECT=your-gcs-project GCS_PROJECT=your-gcs-project GCS_BUCKET=your-gcs-bucket # Optional PROJECT_ROOT=path_to_where_your_code_lives ``` ## Modules PyGyver is structured around several modules available in the `etl` folder. Here is a summary table of those modules: | Module name | Descrition | Documentation | | ------------- |-------------|-------------| | `dw` | Perform task against the Google Cloud BigQuery API | [dw.md](docs/dw.md) | | `facebook` | Perform task against the Facebook Marketing API | [facebook.md](docs/facebook.md) | | `gooddata` | Perform task against the GoodData API | - | | `gs` | Perform task against the Google Sheet API | - | | `lib` | Store utilities used by other modules | - | | `pipeline` | Utility to build data pipelines via YAML definition | [pipeline.md](docs/pipeline.md) | | `prep` | Data transformation - ML pipelines | - | | `storage` | Perform task against the AWS S3 and Google Cloud Storage API | [storage.md](docs/storage.md) | | `toolkit` | Sets of tools for data manipulation | - | In order to load `BigQueryExecutor` from the `dw` module, you can run: ``` from pygyver.etl.dw import BigQueryExecutor ``` ## Contributing > To get started... ### Step 1 - 👯 Clone this repo to your local machine using `git@github.com:madedotcom/pygyver.git` ### Step 2 - **HACK AWAY!** 🔨🔨🔨 The team follows TDD to develop new features on `pygyver`. Tests can be found in `pygyver/tests`. ### Step 3 - 🔃 Create a new pull request and request review from team members. Where applicable, a test should be added with the code change. ## FAQ - **How to release a new version to PyPi?** 1. Merge your changes to `master` branch 2. Create a new release using `https://github.com/madedotcom/pygyver/releases` %package -n python3-pygyver Summary: Data engineering & Data science Pipeline Framework Provides: python-pygyver BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pygyver # PyGyver > PyGyver is a user-friendly python package for data integration and manipulation. > Named after MacGyver, title character in the TV series MacGyver, and Python, the main language used in the repository. ## Installation ### PyPi PyGyver is available on [PyPi](https://pypi.org/project/pygyver/). ```python pip install pygyver ``` ### Setup Most APIs requires access token files to authentificate and perform tasks such as creating or deleting objects. Those files need to be generated prior to using `pygyver` and stored in the environment you are executing your code against. The package make use of environment variables, and some of the below might need be supplied in your environment: ``` # Access token path GOOGLE_APPLICATION_CREDENTIALS=path_to_google_access_token.json FACEBOOK_APPLICATION_CREDENTIALS=path_to_facebook_access_token.json # Default values BIGQUERY_PROJECT=your-gcs-project GCS_PROJECT=your-gcs-project GCS_BUCKET=your-gcs-bucket # Optional PROJECT_ROOT=path_to_where_your_code_lives ``` ## Modules PyGyver is structured around several modules available in the `etl` folder. Here is a summary table of those modules: | Module name | Descrition | Documentation | | ------------- |-------------|-------------| | `dw` | Perform task against the Google Cloud BigQuery API | [dw.md](docs/dw.md) | | `facebook` | Perform task against the Facebook Marketing API | [facebook.md](docs/facebook.md) | | `gooddata` | Perform task against the GoodData API | - | | `gs` | Perform task against the Google Sheet API | - | | `lib` | Store utilities used by other modules | - | | `pipeline` | Utility to build data pipelines via YAML definition | [pipeline.md](docs/pipeline.md) | | `prep` | Data transformation - ML pipelines | - | | `storage` | Perform task against the AWS S3 and Google Cloud Storage API | [storage.md](docs/storage.md) | | `toolkit` | Sets of tools for data manipulation | - | In order to load `BigQueryExecutor` from the `dw` module, you can run: ``` from pygyver.etl.dw import BigQueryExecutor ``` ## Contributing > To get started... ### Step 1 - 👯 Clone this repo to your local machine using `git@github.com:madedotcom/pygyver.git` ### Step 2 - **HACK AWAY!** 🔨🔨🔨 The team follows TDD to develop new features on `pygyver`. Tests can be found in `pygyver/tests`. ### Step 3 - 🔃 Create a new pull request and request review from team members. Where applicable, a test should be added with the code change. ## FAQ - **How to release a new version to PyPi?** 1. Merge your changes to `master` branch 2. Create a new release using `https://github.com/madedotcom/pygyver/releases` %package help Summary: Development documents and examples for pygyver Provides: python3-pygyver-doc %description help # PyGyver > PyGyver is a user-friendly python package for data integration and manipulation. > Named after MacGyver, title character in the TV series MacGyver, and Python, the main language used in the repository. ## Installation ### PyPi PyGyver is available on [PyPi](https://pypi.org/project/pygyver/). ```python pip install pygyver ``` ### Setup Most APIs requires access token files to authentificate and perform tasks such as creating or deleting objects. Those files need to be generated prior to using `pygyver` and stored in the environment you are executing your code against. The package make use of environment variables, and some of the below might need be supplied in your environment: ``` # Access token path GOOGLE_APPLICATION_CREDENTIALS=path_to_google_access_token.json FACEBOOK_APPLICATION_CREDENTIALS=path_to_facebook_access_token.json # Default values BIGQUERY_PROJECT=your-gcs-project GCS_PROJECT=your-gcs-project GCS_BUCKET=your-gcs-bucket # Optional PROJECT_ROOT=path_to_where_your_code_lives ``` ## Modules PyGyver is structured around several modules available in the `etl` folder. Here is a summary table of those modules: | Module name | Descrition | Documentation | | ------------- |-------------|-------------| | `dw` | Perform task against the Google Cloud BigQuery API | [dw.md](docs/dw.md) | | `facebook` | Perform task against the Facebook Marketing API | [facebook.md](docs/facebook.md) | | `gooddata` | Perform task against the GoodData API | - | | `gs` | Perform task against the Google Sheet API | - | | `lib` | Store utilities used by other modules | - | | `pipeline` | Utility to build data pipelines via YAML definition | [pipeline.md](docs/pipeline.md) | | `prep` | Data transformation - ML pipelines | - | | `storage` | Perform task against the AWS S3 and Google Cloud Storage API | [storage.md](docs/storage.md) | | `toolkit` | Sets of tools for data manipulation | - | In order to load `BigQueryExecutor` from the `dw` module, you can run: ``` from pygyver.etl.dw import BigQueryExecutor ``` ## Contributing > To get started... ### Step 1 - 👯 Clone this repo to your local machine using `git@github.com:madedotcom/pygyver.git` ### Step 2 - **HACK AWAY!** 🔨🔨🔨 The team follows TDD to develop new features on `pygyver`. Tests can be found in `pygyver/tests`. ### Step 3 - 🔃 Create a new pull request and request review from team members. Where applicable, a test should be added with the code change. ## FAQ - **How to release a new version to PyPi?** 1. Merge your changes to `master` branch 2. Create a new release using `https://github.com/madedotcom/pygyver/releases` %prep %autosetup -n pygyver-0.1.1.42 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-pygyver -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 29 2023 Python_Bot - 0.1.1.42-1 - Package Spec generated