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|
%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.aliyun.com/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
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.1.42-1
- Package Spec generated
|
