TalentSavvy ImproveTeam

Scripts for data extraction scripts from software development systems

Installation

Install the package using pip:

pip install talentsavvy-improveteam

That's it! The package is published on PyPI, so no special index URLs or authentication is required.

This will automatically install all required dependencies:

  • paramiko
  • pytz
  • python-dateutil
  • pandas
  • requests
  • urllib3

Viewing Package Information

To view package information including installation location:

pip show talentsavvy-improveteam

This displays:

  • Package metadata (version, author, etc.)
  • Installation location (Location field)
  • Required dependencies
  • Entry points (available commands)

Accessing This Documentation

The full README documentation is available in several ways:

  1. PyPI Website: View the package page at https://pypi.org/project/talentsavvy-improveteam/

    • The README content is displayed automatically on the package page

  2. Using pip: The pip show command displays basic package information including location and dependencies

  3. Package Metadata: The README is included in the package's metadata (as long_description)

  4. Installed Files: After installation, the README can be found at:

    <site-packages>/talentsavvy_improveteam-<version>.dist-info/metadata.json

    Or view the raw README content:

    python -c "import pkg_resources; print(pkg_resources.get_distribution('talentsavvy-improveteam').get_metadata('DESCRIPTION.rst'))"

Configuration

The package includes a sample config.json file that contains the configuration template.

Finding the Package Location

To locate where the package files are installed, use the following command to get the Location:

pip show talentsavvy-improveteam

The config.json file is installed at:

<site-packages>/common/config.json

Where <site-packages> is typically:

  • Windows: C:\Python<version>\Lib\site-packages\ (system-wide) or C:\Users\<username>\AppData\Local\Programs\Python\Python<version>\Lib\site-packages\ (user installation)
  • Linux/macOS: /usr/local/lib/python<version>/site-packages/ (system-wide) or ~/.local/lib/python<version>/site-packages/ (user installation)

Setup Instructions

  1. Update config.json with your specific configuration values for each data source you plan to use.

  2. Refer to each extraction script's documentation for the specific configuration keys it requires.

Usage

After installing the package, you can run the data extraction scripts from the command line.

Running Data Extraction Scripts

Each extraction script can be run with the following syntax:

extract_<source> -s <start_date>

Where <source> is one of:

  • azuredevops_boards - Azure DevOps work item events
  • azuredevops_pipelines - Azure DevOps pipeline events
  • azuredevops_repos - Azure DevOps repository events
  • bitbucket_pipelines - Bitbucket pipeline events
  • bitbucket_repos - Bitbucket repository events
  • github - GitHub repository events
  • github_actions - GitHub Actions workflow events
  • gitlab - GitLab repository events
  • gitlab_pipelines - GitLab Pipelines events
  • jenkins - Jenkins build and deployment events
  • jira - Jira work item events
  • octopus - Octopus Deploy deployment events
  • outsystems - OutSystems development events

And <start_date> is optional and should be in YYYY-MM-DD format.

Examples

Extract Jira work items:

extract_jira -s 2025-04-01

Extract GitLab code events:

extract_gitlab -s 2025-04-01

Extract Jenkins builds:

extract_jenkins -s 2025-04-01

If no start date is provided, the scripts will use the last checkpoint date (if available) or a default date.

Output

The extracted data will be saved as CSV files in the directory specified by the EXPORT_PATH key in your config.json file (default: ./export directory).

Each script maintains a checkpoint file to track the last extraction timestamp, allowing for incremental extractions on subsequent runs.

SFTP Upload

After extraction, you can upload the CSV files to an SFTP server:

sftp_upload

This will upload all CSV files from the export directory to the configured SFTP server and delete them locally after successful upload.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for vinay_gupta from gravatar.com vinay_gupta

Credits

Author: TalentSavvy.com

Requires

Python >=3.7

Classifiers

Report project as malware

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