| Name | prossa JSON |
| Version |
1.2.0
JSON |
| download |
| home_page | None |
| Summary | An open-source library for checking data preprocessing techniques applicable on a dataset |
| upload_time | 2024-07-10 16:39:44 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) [2024] [Procca Version 1.0.0] 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 |
data
analysis
preprocessing
machine learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Prossa
Prossa is an open-source library for checking data preprocessing techniques applicable on a dataset.
## Installation
You can install Prossa using pip:
```
pip install prossa
```
## Usage
Here's a quick example of how to use Prossa:
```python
import pandas as pd
from prossa import analyze_dataset
from prossa import check_outliers
# Load your dataset
df = pd.read_csv('your_dataset.csv')
# Analyze the dataset
analyze_dataset(df)
#also you can check if there are outliers in the dataset
outliers = check_outliers(df)
print(outliers)
```
### Methods in current prossa version
```python
# All methods take dataframe as an argument. ie. arg = df or arg = dataset
#Perform a comprehensive analysis of the dataset, checking various techniques in data preprocessing for recommendations.
analyze_dataset(arg)
#Check for missing values in the dataset.
check_missing_values(arg)
#Check for outliers in the dataset.
check_outliers(arg)
#Check data types in the dataset.
check_data_types(arg)
#Check if dataset needs scaling and encoding.
check_scaling_encoding(arg)
#Check categorical data in the dataset.
check_categorical_data(arg)
#Check for constant columns in the dataset.
check_constant_columns(arg)
#Check for imputation and get recommendations.
check_imputation(arg)
```
For more detailed usage instructions, please refer to the documentation.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
You can find the project repository on GitHub:
[GitHub Repository](https://github.com/Fosberg-codex/prossa)
## License
This project is licensed under the MIT License - see the LICENSE file for details.
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"description": "# Prossa\r\n\r\nProssa is an open-source library for checking data preprocessing techniques applicable on a dataset.\r\n\r\n## Installation\r\n\r\nYou can install Prossa using pip:\r\n\r\n```\r\npip install prossa\r\n```\r\n\r\n## Usage\r\n\r\nHere's a quick example of how to use Prossa:\r\n\r\n```python\r\nimport pandas as pd\r\nfrom prossa import analyze_dataset\r\nfrom prossa import check_outliers\r\n\r\n# Load your dataset\r\ndf = pd.read_csv('your_dataset.csv')\r\n\r\n# Analyze the dataset\r\nanalyze_dataset(df)\r\n\r\n#also you can check if there are outliers in the dataset\r\noutliers = check_outliers(df)\r\nprint(outliers)\r\n```\r\n### Methods in current prossa version\r\n\r\n```python\r\n# All methods take dataframe as an argument. ie. arg = df or arg = dataset\r\n\r\n#Perform a comprehensive analysis of the dataset, checking various techniques in data preprocessing for recommendations.\r\nanalyze_dataset(arg)\r\n\r\n#Check for missing values in the dataset.\r\ncheck_missing_values(arg)\r\n\r\n#Check for outliers in the dataset.\r\ncheck_outliers(arg)\r\n\r\n#Check data types in the dataset.\r\ncheck_data_types(arg)\r\n\r\n#Check if dataset needs scaling and encoding.\r\ncheck_scaling_encoding(arg)\r\n\r\n#Check categorical data in the dataset.\r\ncheck_categorical_data(arg)\r\n\r\n#Check for constant columns in the dataset.\r\ncheck_constant_columns(arg)\r\n\r\n#Check for imputation and get recommendations.\r\ncheck_imputation(arg)\r\n\r\n\r\n```\r\n\r\n\r\nFor more detailed usage instructions, please refer to the documentation.\r\n\r\n## Contributing\r\n\r\nContributions are welcome! Please feel free to submit a Pull Request.\r\n\r\nYou can find the project repository on GitHub:\r\n[GitHub Repository](https://github.com/Fosberg-codex/prossa)\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License - see the LICENSE file for details.\r\n",
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