| Name | multi-column-distribution-sampler JSON |
| Version |
0.0.1
JSON |
| download |
| home_page | None |
| Summary | Distribution based sampling for multiple categorical columms |
| upload_time | 2024-06-17 02:19:53 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) 2024 Alan Abraham Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
dataframe
distribution
distribution sampler
multi column sampler
multiple
multiple column sampling
pandas
representative sampling
sampler
sampling
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# MULTI COLUMN DISTRIBUTION SAMPLER
Function to draw a sample from a given dataframe while maintaining the same distribution of the columns of interest.
Also includes a function to determine the minimum sample size needed to ensure the distribution of the columns of interest in the given dataframe.
## Overview
In order to draw a sample from a `pandas dataframe` we can use the `sample` function. But, this doesn't ensure that the sample drawn would be representative of the distributions of the columns of interest in the dataframe. Although we can use the `train_test_split` with `stratify` from `sklearn.model_selection` for stratified sampling, things get complex once we want the sample to follow the exact distribution for multiple columns. This package abstracts away all the logic and provides you with functions that can be used to determine the minimum sample size reqired for a distributive sample and also the sample itself when involving multiple columns. Since this package ensures a perfect representative sample, the resulting sample will probably be a bit larger compared to the sample size desired. This increase in sample size will be depending on the distributions of the columns in the actual dataframe and also the number of columns factored in while sampling.
## Features
* Multi column based representative sampling
* Handles both continuous and categorical features
* Uses Gini index to measure impurity of partion
* Includes basic tree printing functionality for tree visualization
# Requirements
Python 3.x
[Optional] Any text editor or IDE of your choice for editing the code.
# Installation
multi_column_distribution_sampler can be installed using the following command:
```
pip install multi_column_distribution_sampler
```
or
```
pip3 install multi_column_distribution_sampler
```
# Dependencies
multi_column_distribution_sampler depends on the following packages:-
* numpy
* pandas
## License
MIT License
Raw data
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