Skip to content

Latest commit

 

History

History
137 lines (98 loc) · 5.55 KB

README.md

File metadata and controls

137 lines (98 loc) · 5.55 KB
Raw
drawing>

Easy environment : easy-to-use Python environment management toolkit

Easy Environment is a Python tool that provides easy-to-use functionality for managing files and data in different environments. It offers a class that simplifies file operations on the local disk and cloud services such as Google Cloud (Google Cloud Storage and Big Query) or SharePoint.

Features

  • Multi-format loading and saving: Load and save files in various formats with one command line
    • Default supported formats: csv, docx, jpg, json, md, parquet, pdf, pickle, png, pptx, sql, toml, txt, xlsx, xml, yaml, yml
    • Unsupported formats: Customisable. See Customise supported formats.
  • Multi-environment management:
    • Local disk: Loading/saving and management.
    • Google Cloud Storage: Loading/saving and management.
    • Big Query: Append, write, and run queries on Big Query tables.
    • SharePoint: Download, upload, and manage files on SharePoint.

drawing

Initialisation

To use Easy Environment, follow these instructions:

  1. Install easyenvi
pip install easyenvi==1.0.5
  1. Create an instance of the EasyEnvironment class

All the parameters in the EasyEnvironment class are optional: it depends on how you use the tool.

from easyenvi import EasyEnvironment

envi = EasyEnvironment(
  local_path="", # Optional

  gcloud_project_id="your-project-id", # Optional
  gcloud_credential_path="path/to/credentials.json", # Optional
  GCS_path="gs://your-bucket-name/", # Optional

  sharepoint_site_url="https://{tenant}.sharepoint.com/sites/{site}", # Optional
  sharepoint_client_id="your-client-id", # Optional
  sharepoint_client_secret="your-client-secret", # Optional
                  )

Specifying certain parameters means certain dependencies:

  • For using local operation, it is necessary to specify local_path, the path from which local operations should be executed - specify an empty string if you want to use the current directory. Additionnaly, the installation of the fsspec library is required.
  • For using Google Cloud, it is necessary to specify the project ID, the path to a credential .json file, and, in case of interaction with Google Cloud Storage, the path to the GCS folder (see Google Cloud Initialisation). Additionnaly, the installation of the libraries google-cloud-storage, google-cloud-bigquery and fsspec is required.
  • For using SharePoint, it is necessary to specify the SharePoint site to interact with, as well as authentication credentials: either the client_id/client_secret pair or the username/user_password pair (see SharePoint Initialisation). Furthermore, the installation of the Office365-REST-Python-Client library is required.

Examples of use

Local features

# Load any file format
my_dict = envi.local.load(path='inputs/my_dictionnary.pickle')
my_logo = envi.local.load(path='inputs/my_logo.png')
dataset = envi.local.load(path='inputs/dataset.csv')

# Save any file format
envi.local.save(obj=my_dict, path='outputs/my_dictionnary.pickle')
envi.local.save(obj=my_logo, path='outputs/my_logo.png')
envi.local.save(obj=dataset, path='outputs/dataset.csv')

Google Cloud Storage features

# Load any file format
my_dict = envi.gcloud.GCS.load(path='inputs/my_dictionnary.pickle')
my_logo = envi.gcloud.GCS.load(path='inputs/my_logo.png')
dataset = envi.gcloud.GCS.load(path='inputs/dataset.csv')

# Save any file format
envi.gcloud.GCS.save(obj=my_dict, path='outputs/my_dictionnary.pickle')
envi.gcloud.GCS.save(obj=my_logo, path='outputs/my_logo.png')
envi.gcloud.GCS.save(obj=dataset, path='outputs/dataset.csv')

Big Query features

df = pd.DataFrame(data={'age': [21, 52, 30], 'wage': [12, 17, 11]})

# Create a new table
envi.gcloud.BQ.write(dataset, 'mydata.mytable')

# Append an existing table
envi.gcloud.BQ.append(dataset, 'mydata.mytable')

# Run queries
query = """
SELECT *
FROM mydata.mytable
WHERE age < 40
"""

new_dataset = envi.gcloud.BQ.query(query).to_dataframe()

SharePoint features

# Download a file
envi.sharepoint.download(input_path="/Document partages/sharepoint_folder/my_file.txt",
                         output_path="local_folder/my_file.txt")
                        
# Upload a file
envi.sharepoint.upload(input_path="local_folder/my_file.txt",
                       output_path="Document partages/folder/my_file.txt")
                      
# List files
envi.sharepoint.list_files(folder="local_folder")

Documentation

The documentation is available here : Easy Environment - Documentation

Credits

  • Thanks to Herve Mignot for his advice on using fsspec.
  • Thanks to Nizar Fawal for encouraging me to deploy this package as a Pypi library.

Future Improvements

Future releases of Easy Environment will include support for additional cloud storage providers, including Amazon Web Services (AWS) and Microsoft Azure.