# Topsis Sort B - LIB PYPI
The TOPSIS-Sort-B is an enhanced variation of the TOPSIS-Sort method, designed to address classification and sorting problems in multiple criteria decision-making. In this method, boundary profiles are employed to determine categorization classes and to sort alternatives based on the proximity of their proximity coefficients to the established profiles.
## Tecnologias usads
| Tecnologias | Versão | Install |
|-------------|---------|---------------------------------------|
| Python | 3.12.1 | `pip install python==3.12.1` |
| Numpy | 1.26.4 | `pip install numpy==1.26.4` |
| Pandas | 2.2.1 | `pip install pandas==2.2.1` |
## Installtion
1. Install the required dependencies by running the following command: pip `pip install topsisSortLib`
## How to Run the Application
1. After installing the package, import the library.
2. from topsisSortLib import topsis_b_sort_profile_classification
## How to Use
1. To utilize the topsis_b_sort_profile_classification function, follow these steps:
2. Import pandas: Begin by importing the pandas library as pd.
# How to Use
To utilize the `topsis_b_sort_profile_classification` function, follow these steps:
1. **Import pandas**: Begin by importing the pandas library.
```python
import pandas as pd
```
2. **Load CSV File**: Load your CSV file into a pandas DataFrame using `pd.read_csv()`.
```python
df = pd.read_csv('your_file.csv')
```
3. **Clean Data**: Clean the DataFrame by converting all non-numeric values to numeric using `pd.to_numeric()` and filling any missing values with zero.
```python
df = df.apply(pd.to_numeric, errors='coerce').fillna(0)
```
4. **Call Function**: Pass the cleaned DataFrame along with other necessary arguments into the `topsis_b_sort_profile_classification` function.
```python
result = topsis_b_sort_profile_classification(
decision_matrix=df,
domain_matrix=your_domain_matrix,
dominant_profiles=your_dominant_profiles,
weights=your_weights
)
```
Make sure to replace `your_file.csv`, `your_domain_matrix`, `your_dominant_profiles`, and `your_weights` with the appropriate variables or data structures.
`
## References
- Silva, D. F. L., & Filho, A. T. A. (2020). Sorting with TOPSIS through boundary and characteristic profiles. Journal Name, Volume(1), 141.
- GeeksforGeeks.TOPSIS method for Multiple-Criteria Decision Making (MCDM). Retrieved from [[URL](https://www.geeksforgeeks.org/topsis-method-for-multiple-criteria-decision-making-mcdm/)]
## Deploy
- Aplicação
- library [[URL](https://pypi.org/project/topsisSortLib/)]
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"description": "# Topsis Sort B - LIB PYPI\r\n\r\n The TOPSIS-Sort-B is an enhanced variation of the TOPSIS-Sort method, designed to address classification and sorting problems in multiple criteria decision-making. In this method, boundary profiles are employed to determine categorization classes and to sort alternatives based on the proximity of their proximity coefficients to the established profiles.\r\n\r\n## Tecnologias usads\r\n| Tecnologias | Vers\u00e3o | Install |\r\n|-------------|---------|---------------------------------------|\r\n| Python | 3.12.1 | `pip install python==3.12.1` |\r\n| Numpy | 1.26.4 | `pip install numpy==1.26.4` |\r\n| Pandas | 2.2.1 | `pip install pandas==2.2.1` |\r\n\r\n\r\n## Installtion\r\n1. Install the required dependencies by running the following command: pip `pip install topsisSortLib`\r\n## How to Run the Application\r\n1. After installing the package, import the library.\r\n2. from topsisSortLib import topsis_b_sort_profile_classification\r\n\r\n## How to Use\r\n1. To utilize the topsis_b_sort_profile_classification function, follow these steps:\r\n2. Import pandas: Begin by importing the pandas library as pd.\r\n # How to Use\r\n\r\nTo utilize the `topsis_b_sort_profile_classification` function, follow these steps:\r\n\r\n1. **Import pandas**: Begin by importing the pandas library.\r\n\r\n ```python\r\n import pandas as pd\r\n ```\r\n\r\n2. **Load CSV File**: Load your CSV file into a pandas DataFrame using `pd.read_csv()`.\r\n\r\n ```python\r\n df = pd.read_csv('your_file.csv')\r\n ```\r\n\r\n3. **Clean Data**: Clean the DataFrame by converting all non-numeric values to numeric using `pd.to_numeric()` and filling any missing values with zero.\r\n\r\n ```python\r\n df = df.apply(pd.to_numeric, errors='coerce').fillna(0)\r\n ```\r\n\r\n4. **Call Function**: Pass the cleaned DataFrame along with other necessary arguments into the `topsis_b_sort_profile_classification` function.\r\n\r\n ```python\r\n result = topsis_b_sort_profile_classification(\r\n decision_matrix=df,\r\n domain_matrix=your_domain_matrix,\r\n dominant_profiles=your_dominant_profiles,\r\n weights=your_weights\r\n )\r\n ```\r\n\r\nMake sure to replace `your_file.csv`, `your_domain_matrix`, `your_dominant_profiles`, and `your_weights` with the appropriate variables or data structures.\r\n\r\n `\r\n## References\r\n\r\n- Silva, D. F. L., & Filho, A. T. A. (2020). Sorting with TOPSIS through boundary and characteristic profiles. Journal Name, Volume(1), 141.\r\n- GeeksforGeeks.TOPSIS method for Multiple-Criteria Decision Making (MCDM). Retrieved from [[URL](https://www.geeksforgeeks.org/topsis-method-for-multiple-criteria-decision-making-mcdm/)]\r\n\r\n## Deploy\r\n- Aplica\u00e7\u00e3o\r\n- library [[URL](https://pypi.org/project/topsisSortLib/)]\r\n",
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