Skip to main content

Scripts for data extraction scripts from software development systems

Project description

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:

  • jira - Jira work item events
  • gitlab - GitLab code events
  • github - GitHub pull request events
  • github_actions - GitHub Actions workflow events
  • jenkins - Jenkins build events
  • azuredevops_boards - Azure DevOps work item events
  • azuredevops_pipelines - Azure DevOps pipeline events
  • azuredevops_repos - Azure DevOps repository events
  • bitbucket_repos - Bitbucket repository events
  • bitbucket_pipelines - Bitbucket pipeline events
  • octopus - Octopus Deploy deployment 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.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

talentsavvy_improveteam-0.33.81.tar.gz (112.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

talentsavvy_improveteam-0.33.81-py3-none-any.whl (116.9 kB view details)

Uploaded Python 3

File details

Details for the file talentsavvy_improveteam-0.33.81.tar.gz.

File metadata

File hashes

Hashes for talentsavvy_improveteam-0.33.81.tar.gz
Algorithm Hash digest
SHA256 a4dd54a6409eca55e54af10f0a910c29541154ebe6cfe445b27fa59832497fcc
MD5 d1e02a4182e2e9fc473b99595dc446f1
BLAKE2b-256 c3a21e2855467a81ba439dd672619c0ae6b246c9b48acdef2a389f27c9d67583

See more details on using hashes here.

File details

Details for the file talentsavvy_improveteam-0.33.81-py3-none-any.whl.

File metadata

File hashes

Hashes for talentsavvy_improveteam-0.33.81-py3-none-any.whl
Algorithm Hash digest
SHA256 6c59a7ef0dcf2d03fc5c78a936a3e005e192bcb3771096a70f2dfb9f97b1750f
MD5 266e7152929af74cef9c0018af91ae03
BLAKE2b-256 d59311ed9c8cd551e9b19bfd3bfd7403c110f476ac00c360e164076c8533e100

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page