| Name | ProdPack JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | An Efficiency and Productivity Analysis Package |
| upload_time | 2024-08-24 13:37:25 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.6 |
| license | MIT License Copyright (c) 2024 Dr Daoping Wang (daopingwang@outlook.com) 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 |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# ProdPack: An Efficiency and Productivity Analysis Package
ProdPack is a Python package designed for Efficiency and Productivity Analysis. Its comprehensive toolset allows for efficient handling of various productivity index estimates, including those that account for undesirable outputs (e.g., total factor productivity, TFP).
## Installation
Install the package by `pip`,
```sh
pip install ProdPack
```
Or install the package by `conda`,
```sh
conda install ProdPack
```
## Usage
A brief example is provided below. For more information, please refer to the [documentation]() and [example notebooks]().
```python
# import the module
import pandas as pd
from ProdPack.model import ProdNP
from DEAPack.utilities import load_example_data
# load the example dataset
data = load_example_data()
# initilise a nonparametric model
model = ProdNP()
# specify the model
model.DMUs = data['region']
model.x_vars = data[['K', 'L']]
model.y_vars = data[['Y']]
model.b_vars = data[['CO2']]
model.time = data['year']
model.g_x = model.x_vars*0
model.ref_type = 'Sequential'
# solve the model
model.solve()
# check the results
data['prod_ch'] = model.prod_ch
data['eff_ch'] = model.eff_ch
data['te_ch'] = model.te_ch
print(data)
# the results are combind into the data set
```
## Communication
You're very welcome to contribute to this package. We appreciate any efforts to improve this package. You can help by adding new features, reporting bugs, or extending the documentation and usage examples. Please contact us if you have any ideas.
- [Pull requests](https://github.com/daopingw/ProdPack/pulls) for pull requests.
- [Issues](https://github.com/daopingw/ProdPack/issues) for bug reports.
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