Exploration_Stats
Overview
This package provides EDA lifecycle functions like visualizations of missing values, their counts, distribution plots of different numerical columns, their skewness and kurtosis, and many more which will save a lot of time
An Exploration is a sequence of steps, along with a sequence of decisions indicating where the explorer was at each step, a sequence of transitions indicating which transition was taken at each step, and a sequence of states indicating an extra state at each step. These representations were developed with Metroidvania games in mind.
Core capabilities include:
Representing exploration processes as a series of steps including partial information about not-yet-explored decisions.
Creating maps and explorations from various text formats, including exploration journal formats.
Reasoning about reachability modulo transition requirements in terms of powers that must be possessed and/or tokens that must be spent for a transition. TODO
The ability to represent fairly sophisticated game logic in the DecisionGraph, and even construct playable maps. Game logic that can't be captured this way can still be represented by making custom changes to maps between exploration steps.
TODO
Dependencies:
Python version 3.8+
network For underlying graph structures.
pytest for testing, install with the [test] option to get it automatically.
Installing
Just run pip install exploration-stats. The tool script should be installed along with the module.
Getting Started:
after installing the package
import exploration_stats as exp and call the exp. eda() function
pass the data frame and you are all set to go
example:
pip install exploration_stats:
import exploration_stats as exp:
exp. eda(df) ---> df is your data frame and eda is the pre-built function that comes with this package
Plans
Better support for open-source eda guides, where steps are not as closely linked to virtual space structure.
Changelog
There are also now a few built-in debug commands available for printing relevant stuff. More may be added as they become popular. It also introduces equivalences, stored in the DecisionGraph, which allow powers (but not tokens) to count as being obtained when one of a set of other requirements is met instead.
v0.1.1 is Still pre-alpha as it's in the process of being re-architected a bit, but some core functionality is present if rough (e.g., core.DecisionGraph and core. Exploration).
v0.1 Initial pre-alpha upload.
Raw data
{
"_id": null,
"home_page": "",
"name": "exploration-stats",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "python,video,stream,video stream,camera stream,sockets",
"author": "Lalit Sharma (Tata Motors)",
"author_email": "lalit.sharma1@tatamotors.com",
"download_url": "",
"platform": null,
"description": "Exploration_Stats\n\nOverview\n\nThis package provides EDA lifecycle functions like visualizations of missing values, their counts, distribution plots of different numerical columns, their skewness and kurtosis, and many more which will save a lot of time \n\nAn Exploration is a sequence of steps, along with a sequence of decisions indicating where the explorer was at each step, a sequence of transitions indicating which transition was taken at each step, and a sequence of states indicating an extra state at each step. These representations were developed with Metroidvania games in mind.\n\nCore capabilities include:\n\nRepresenting exploration processes as a series of steps including partial information about not-yet-explored decisions.\nCreating maps and explorations from various text formats, including exploration journal formats.\nReasoning about reachability modulo transition requirements in terms of powers that must be possessed and/or tokens that must be spent for a transition. TODO\nThe ability to represent fairly sophisticated game logic in the DecisionGraph, and even construct playable maps. Game logic that can't be captured this way can still be represented by making custom changes to maps between exploration steps. \n\nTODO\nDependencies:\nPython version 3.8+\nnetwork For underlying graph structures.\npytest for testing, install with the [test] option to get it automatically.\nInstalling\nJust run pip install exploration-stats. The tool script should be installed along with the module.\n\n\nGetting Started:\nafter installing the package \nimport exploration_stats as exp and call the exp. eda() function \npass the data frame and you are all set to go \n\nexample:\n\npip install exploration_stats:\n\nimport exploration_stats as exp:\n\nexp. eda(df) ---> df is your data frame and eda is the pre-built function that comes with this package \n\nPlans\nBetter support for open-source eda guides, where steps are not as closely linked to virtual space structure.\n\nChangelog\nThere are also now a few built-in debug commands available for printing relevant stuff. More may be added as they become popular. It also introduces equivalences, stored in the DecisionGraph, which allow powers (but not tokens) to count as being obtained when one of a set of other requirements is met instead.\nv0.1.1 is Still pre-alpha as it's in the process of being re-architected a bit, but some core functionality is present if rough (e.g., core.DecisionGraph and core. Exploration).\nv0.1 Initial pre-alpha upload.\n\n\n\n",
"bugtrack_url": null,
"license": "",
"summary": "An EDA Package which can perform multiple Eda Operations on your dataframe in one go and yes saves Time as well !!! ------ Developed by Lalit Sharma",
"version": "0.0.34",
"split_keywords": [
"python",
"video",
"stream",
"video stream",
"camera stream",
"sockets"
],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "2435724c8a505c2099d300867d54d80f",
"sha256": "920b0e6ecd8854553bf6d413eb2e395c6b8f8722b568a59a548958e4e98dcea4"
},
"downloads": -1,
"filename": "exploration_stats-0.0.34-py3-none-any.whl",
"has_sig": false,
"md5_digest": "2435724c8a505c2099d300867d54d80f",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 3494,
"upload_time": "2022-12-31T20:47:22",
"upload_time_iso_8601": "2022-12-31T20:47:22.883734Z",
"url": "https://files.pythonhosted.org/packages/3d/36/0dafe646003c25d3757dbeed5819c16dc0db2ea44cf7936589a896a40c4e/exploration_stats-0.0.34-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2022-12-31 20:47:22",
"github": false,
"gitlab": false,
"bitbucket": false,
"lcname": "exploration-stats"
}
JSON